QPanda3 Runtime MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes, such as sample_tool for sampling and estimate_tool for expectation estimation, with clear separation between task execution and management tools. However, there is some overlap between batch_sample_tool and sample_tool, as both handle sampling but with different batch capabilities, which could cause minor confusion. The binding-related tools (create_circuit_observable_binding_tool, add_product_rule_tool, add_zip_rule_tool) are well-differentiated within their domain.
Naming Consistency3/5
The naming follows a mostly consistent snake_case pattern, but there are inconsistencies in verb usage and structure. For example, tools like active_account_info_tool use 'info' as a noun, while others like list_qpu_devices_tool use 'list' as a verb, and some like setup_origin_quantum_account_tool are overly verbose. This mixed convention reduces predictability, though the snake_case format is maintained throughout.
Tool Count3/5
With 19 tools, the count is on the higher side for a quantum computing server, which may feel heavy but is somewhat justified by the domain's complexity covering execution, binding, and account management. However, it borders on being excessive, as some tools could potentially be consolidated (e.g., batch_sample_tool and sample_tool), making the set less streamlined than ideal for an agent.
Completeness5/5
The tool set provides comprehensive coverage for quantum circuit execution and management, including CRUD-like operations for tasks (create, cancel, list, get status/results), bindings (create, delete, list, add rules), and devices (list, get properties). It supports both sampling and estimation workflows, with batch and multi-objective capabilities, leaving no obvious gaps for the server's purpose in quantum computing runtime operations.
Average 4.2/5 across 19 of 19 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool deletes a binding from session storage, implying a destructive mutation, but doesn't clarify permissions, reversibility, side effects, or error conditions beyond the return dictionary. This is inadequate for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core action, followed by structured Args and Returns sections. Every sentence earns its place: the first states the purpose, the second adds context, and the bullet points clarify outcomes without redundancy. It's appropriately sized and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a destructive tool with no annotations, 0% schema coverage, but an output schema (implied by Returns section), the description is partially complete. It covers the parameter meaning and return format, but lacks behavioral details like error handling or session storage implications. This meets a minimum viable level with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context for the single parameter 'binding_id', explaining it as 'The ID of the binding to delete'. With schema description coverage at 0% and no output schema to explain parameters, this compensates well. However, it doesn't specify format or constraints (e.g., UUID), leaving minor gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Delete' and resource 'CircuitObservableBinding object', making the purpose unambiguous. It distinguishes from siblings like 'create_circuit_observable_binding_tool' and 'list_bindings_tool' by specifying deletion. However, it doesn't explicitly mention 'session storage' as a distinguishing factor from other storage mechanisms, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing binding), exclusions, or relationships with sibling tools like 'list_bindings_tool' for checking bindings first. Usage is implied but not explicitly stated, leaving gaps for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively explains that the tool returns authentication status and account information, clarifies the session-based authentication context, and details the return structure. This adds valuable context beyond what the input schema (empty) provides, though it could mention potential errors or limitations more explicitly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a purpose statement, context explanation, and return details, but it could be more front-loaded. The first sentence states the purpose, but the following sentences about authentication context and return values, while useful, might be verbose for a tool with no inputs. Some trimming could improve efficiency without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, no annotations, and an output schema exists (implied by 'Has output schema: true'), the description is reasonably complete. It explains the purpose, provides authentication context, and details the return structure, which compensates for the lack of annotations. However, it could better integrate with sibling tools to enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so the baseline is 4 as per rules. The description doesn't need to add parameter information since there are none, and it appropriately focuses on output semantics instead, which is acceptable given the context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'List saved account information' which is a clear verb+resource combination. However, it doesn't distinguish from sibling tools like 'active_account_info_tool' or 'setup_origin_quantum_account_tool', leaving ambiguity about when to use this versus those alternatives. The purpose is understandable but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. The description mentions 'session-based authentication' context, which implies usage for checking authentication status, but it doesn't specify scenarios, prerequisites, or exclusions. Without clear when/when-not instructions, users might struggle to choose between this and tools like 'active_account_info_tool'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool lists bindings from the current session, implying read-only behavior, but lacks details on permissions, rate limits, or error handling. It adds some context but is incomplete for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by return details. It is concise but includes a redundant 'Returns:' section that partially repeats the output schema, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, output schema provided), the description is mostly complete. It explains what the tool does and the return structure, though it could benefit from more behavioral context, such as session scope implications or error scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description does not add parameter details, which is appropriate, earning a baseline score of 4 for adequately handling the lack of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('CircuitObservableBinding objects'), specifying it returns all bindings created in the current session. However, it does not explicitly differentiate from sibling tools like 'delete_binding_tool' or 'create_circuit_observable_binding_tool' in terms of scope or purpose, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing bindings to exist, or contrast with other tools like 'delete_binding_tool' or 'estimate_with_binding_tool' for context-specific usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool retrieves 'recent' tasks and includes a default limit, but lacks details on permissions, rate limits, pagination, or error handling. The description adds some behavioral context but is incomplete for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for purpose, arguments, returns, and an example. It's appropriately sized and front-loaded with the core purpose. Some redundancy exists (e.g., repeating 'list' in the description and example), but overall it's efficient with minimal waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no nested objects) and the presence of an output schema (implied by the detailed return description), the description is reasonably complete. It covers the purpose, parameter, and return format adequately, though it could benefit from more behavioral context given the lack of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema. The schema has 0% description coverage for the single parameter 'limit', but the description explains it as 'Maximum number of tasks to return (default: 10).' This fully compensates for the schema gap, providing clear semantics for the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List user's recent tasks' and 'Retrieve a list of recently submitted quantum computing tasks.' This specifies the verb (list/retrieve) and resource (tasks), though it doesn't explicitly differentiate from sibling tools like get_task_status_tool or get_task_results_tool. The purpose is clear but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like get_task_status_tool or get_task_results_tool, nor does it specify prerequisites, context, or exclusions. The example shows basic usage but offers no strategic advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format in detail, which is helpful, but lacks information about potential side effects, authentication requirements, rate limits, or error conditions beyond the 'status' field. The description doesn't contradict any annotations (since none exist), but it's incomplete for a tool that might have operational constraints in a quantum computing context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a clear purpose statement, provides return details, and includes a practical example. Each sentence adds value: the first defines the tool, the second adds context about account accessibility, the return section documents output structure, and the example shows usage. There's minimal redundancy, though the example could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there's an output schema (implied by 'Has output schema: true'), the description doesn't need to fully explain return values, but it does so anyway, which adds value. With 0 parameters and no annotations, the description covers the essential purpose and output well. However, it could improve by addressing behavioral aspects like error handling or usage context relative to siblings, making it slightly incomplete for optimal agent guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description correctly doesn't discuss parameters, focusing instead on the output. This meets the baseline for tools with no parameters, as it doesn't add unnecessary information beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'List all available QPU (Quantum Processing Unit) devices' and 'Retrieves a list of all quantum computing devices accessible through the current account.' This specifies the verb ('List', 'Retrieves') and resource ('QPU devices', 'quantum computing devices'). However, it doesn't explicitly differentiate from sibling tools like 'get_qpu_properties_tool', which might provide detailed properties of a specific device rather than listing all devices.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions using the output for other tools ('Use devices["devices"][0]["id"] to get device ID for other tools'), but doesn't specify which sibling tools are alternatives or when this tool is preferred. For example, it doesn't compare to 'get_qpu_properties_tool' or explain if this is for initial discovery versus detailed queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool as a read-only operation ('Get information') and specifies the return structure, which is helpful. However, it doesn't mention potential errors, authentication requirements, rate limits, or whether the information is cached or real-time. The description adds basic context but lacks depth on behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise and front-loaded: the first sentence states the core purpose, followed by clarifying details about what's returned. The 'Returns:' section is somewhat redundant with the preceding sentence but provides structured output details. Overall, it's efficient with minimal waste, though the repetition slightly reduces optimal structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It explains the purpose, specifies the scope ('currently active account'), and outlines the return structure. With an output schema present, it doesn't need to exhaustively detail return values. However, it could benefit from more behavioral context (e.g., error conditions) to be fully comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description appropriately doesn't discuss parameters, which is correct for a parameterless tool. It adds value by clarifying that this tool retrieves information about the 'currently active' account, providing semantic context beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get information about the currently active account' with specific details about what information is returned (channel type, connection status). It distinguishes itself from siblings like 'list_saved_accounts_tool' by focusing on the currently active session account rather than listing all saved accounts. However, it doesn't explicitly contrast with other account-related tools beyond this implicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'currently active account' and 'account being used in the current session,' suggesting this tool should be used when needing details about the account currently in use. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'list_saved_accounts_tool' or 'setup_origin_quantum_account_tool,' nor does it mention any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explains that this tool configures authentication and describes the return format (status, message, channel, available_devices). However, it doesn't disclose important behavioral traits like whether this is a one-time setup or persists credentials, what happens on failure, or any rate limits/authentication requirements beyond the basic API key.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections: purpose statement, credential methods, parameter explanations, return format, and examples. It's appropriately sized for a configuration tool, though the 'Args:' and 'Returns:' sections could be more integrated with the narrative flow rather than appearing as separate documentation blocks.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (authentication setup), no annotations, and an output schema exists, the description provides good coverage. It explains what the tool does, how to use it, parameters, and return values. However, it could better address prerequisites (like needing an Origin Quantum account) and relationship to sibling tools that might use the configured authentication.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 3 parameters, the description provides excellent parameter semantics. It explains each parameter's purpose: api_key for authentication with fallback to environment variable, channel type always 'qcloud', and config_path for file-based configuration. The examples demonstrate practical usage patterns for different parameter combinations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Configure Origin Quantum qcloud account authentication' and 'Set up credentials to access Origin Quantum computing services.' It specifies the verb ('configure', 'set up') and resource ('account authentication', 'credentials'), but doesn't explicitly differentiate from sibling tools like 'list_saved_accounts_tool' or 'active_account_info_tool'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool: to set up authentication for accessing quantum services. It explains three methods for providing credentials (direct, config file, environment variables) with examples. However, it doesn't explicitly state when NOT to use it or mention alternatives among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it's a read operation (implied by 'get'/'check'), returns a dictionary with specific fields (status, task_id, task_status, progress, error_message), and includes possible task_status values. It does not cover aspects like rate limits, authentication needs, or error handling beyond the status, but provides substantial context for a status-checking tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear purpose statement, Args and Returns sections, and an example. It is appropriately sized and front-loaded, with the key information presented first. However, the example includes conditional logic that, while useful, adds slight verbosity, preventing a perfect score of 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple status check), no annotations, and the presence of an output schema (implied by the Returns section), the description is complete enough. It explains the purpose, parameter semantics, return values, and usage context, covering all necessary aspects for an AI agent to invoke it correctly without redundancy.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It adds meaning by explaining that 'task_id' is 'The ID of the task (returned by sample_tool or estimate_tool)', which clarifies the parameter's origin and usage. This is helpful beyond the schema's type information, but does not detail format constraints or examples, keeping it from a 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the execution status of a task' and 'Check the current status of a submitted quantum computing task.' It specifies the verb ('get'/'check') and resource ('task status'), but does not explicitly differentiate from sibling tools like 'list_my_tasks_tool' or 'cancel_task_tool', which prevents a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: after submitting a task via 'sample_tool' or 'estimate_tool', as indicated in the Args section. It also hints at an alternative action ('get_task_results_tool') in the example when status is 'DONE'. However, it does not explicitly state when not to use it or compare to all relevant siblings like 'list_my_tasks_tool', so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that cancellation is a request (not guaranteed) and only works on incomplete tasks, adding useful behavioral context. However, it does not cover aspects like permissions, rate limits, or error handling beyond the return structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a purpose statement, usage condition, parameter explanation, return details, and an example—all in a compact format. Every sentence adds value without redundancy, and it is front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation with no annotations), the description is mostly complete: it explains purpose, usage, parameters, and returns. The output schema exists, so return values are documented. However, it lacks details on side effects or error cases, leaving minor gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining the single parameter 'task_id' as 'The ID of the task to cancel', adding clear meaning. It does not provide format details (e.g., string pattern), but the example helps. With 0% coverage and 1 parameter, this is above the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('cancel') and resource ('a running or pending task'), distinguishing it from sibling tools like get_task_status_tool or get_task_results_tool. It specifies the action is a request for cancellation, not an immediate termination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit context on when to use it ('only effective if the task has not yet completed'), but does not mention alternatives like waiting for completion or using other task management tools. The guidance is clear but lacks sibling tool comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that this is an additive operation ('Add product rule') and describes the combinatorial behavior with an example. However, it lacks details on permissions, error handling, or side effects (e.g., whether it modifies an existing binding irreversibly). The return dictionary is documented, which adds some behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a clear purpose statement, provides an analogy and example, lists args and returns in labeled sections, and includes a practical code example. Every sentence adds value without redundancy, making it easy to scan and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (combinatorial operation with 3 parameters), no annotations, and an output schema present (implied by the Returns section), the description is complete. It covers purpose, usage, parameters, return values, and provides an example. The output schema details are fully described in the Returns section, eliminating the need for further explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all three parameters: 'binding_id' is explained as 'The ID returned by create_circuit_observable_binding_tool', and 'circuit_indices' and 'observable_indices' are described with examples showing they are lists of integers for combinations. This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Add product (Cartesian product) rule to CircuitObservableBinding' with a specific verb ('Add'), resource ('product rule'), and target ('CircuitObservableBinding'). It distinguishes from siblings like 'add_zip_rule_tool' by specifying the Cartesian product operation and provides an analogy to Python's itertools.product for clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to generate all combinations of circuits with observables, analogous to itertools.product. It implicitly distinguishes from 'add_zip_rule_tool' (likely for pairing rather than full product) but does not explicitly state when not to use it or name alternatives. The example reinforces the usage context effectively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool only works for completed tasks (DONE status), which is a key behavioral constraint. It also describes return formats for different task types, though it doesn't mention error handling, rate limits, or authentication needs explicitly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately front-loaded with the core purpose, but includes extensive return value documentation that duplicates what an output schema would provide. The example is helpful but lengthy. Some sentences could be more concise while maintaining clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (retrieving quantum computation results) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, prerequisites, and usage context well, though it could benefit from more behavioral details like error cases or performance characteristics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explains that 'task_id' refers to 'The ID of the completed task,' adding semantic context beyond the schema's type information. However, it doesn't specify format constraints (e.g., length, pattern) or provide examples of valid task IDs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get the computation results') and resource ('of a completed task'), distinguishing it from sibling tools like get_task_status_tool (which checks status) and cancel_task_tool (which cancels tasks). The verb 'retrieve' reinforces the purpose without being tautological.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('The task must be in DONE status to retrieve results') and provides an example showing usage after checking status with get_task_status_tool. This clearly differentiates it from alternatives and establishes prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining what the tool does (generates one-to-one pairs), providing an example, and specifying the return structure. It doesn't mention permissions, rate limits, or error conditions, but covers core behavior adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with a clear purpose statement, analogy, parameter explanations, return values, and a practical example. Every sentence adds value without redundancy, and it's front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (creating indexed pairings), 0% schema coverage, and no annotations, the description provides complete guidance: purpose, analogy, parameter semantics, return structure, and an example. The output schema is present, so return values don't need extra explanation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all three parameters: binding_id's source, circuit_indices and observable_indices as lists that must match in length, and their pairing behavior. The example further clarifies usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'add' and resource 'zip (one-to-one) rule to CircuitObservableBinding', with a specific analogy to Python's zip function. It distinguishes from sibling tools like add_product_rule_tool by specifying it creates one-to-one pairs rather than Cartesian products.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by mentioning it's analogous to Python's zip function and giving a concrete example, which helps understand when to use it. However, it doesn't explicitly state when NOT to use it or compare alternatives like add_product_rule_tool beyond the analogy.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively discloses key behavioral traits: it's an execution tool (implies mutation/write), returns a task ID for tracking, requires follow-up tools for results, and mentions efficiency benefits. It doesn't cover rate limits, authentication needs, or error handling details, but provides substantial operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with the core purpose, explains efficiency benefit, details parameters with clear formatting, specifies return values, provides a practical example, and adds important usage notes. Every section adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (batch execution with 4 parameters), no annotations, and an output schema present, the description is remarkably complete. It covers purpose, parameters, returns, example usage, and integration with sibling tools. The output schema handles return structure, so the description appropriately focuses on operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 4 parameters in detail. It clarifies formats (QProg/QCircuit/OriginIR), provides examples, explains defaults (shots: 1000), optional vs. required parameters, and the behavior when 'measure_qubits' is unspecified. The example further illustrates parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Execute batch quantum circuit sampling task. Submit multiple quantum circuits for sampling in a single batch.' It specifies the verb ('execute batch sampling'), resource ('quantum circuits'), and distinguishes it from the sibling 'sample_tool' by emphasizing batch efficiency over individual submissions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for efficiency when submitting multiple circuits. It explicitly mentions alternatives ('get_task_status_tool and get_task_results_tool') for progress checking and result retrieval. However, it doesn't explicitly state when NOT to use it (e.g., for single circuits) or compare it directly to 'sample_tool'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining the tool creates a binding with a unique ID for referencing, describes the return structure, and mentions follow-up tools needed. It doesn't cover error conditions, performance characteristics, or authentication requirements, but provides substantial behavioral context for a creation operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, args, returns, example, note) and every sentence adds value. It could be slightly more concise in the opening paragraph but remains efficient overall with no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of quantum multi-objective decision scenarios, 2 parameters with 0% schema coverage, no annotations, but with output schema, the description provides excellent completeness. It explains the purpose, parameters, return values, workflow context, and includes a practical example - covering everything needed to understand and use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 2 parameters, the description fully compensates by explaining both parameters in detail: 'circuits' as 'List of quantum circuits in QProg/QCircuit/OriginIR format' where 'Each circuit represents a different strategy to evaluate', and 'observables' as 'List of observables (Hamiltonians)' where 'Each observable represents a different objective dimension to measure' with format examples. This adds significant meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a 'CircuitObservableBinding' for 'multi-objective quantum decision making' and specifies it manages 'multiple quantum circuits and multiple observables'. It distinguishes this from siblings like add_product_rule_tool and estimate_with_binding_tool by explaining this is the initial creation step before those operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('for quantum multi-objective decision scenarios where you need to evaluate multiple strategies against multiple objective dimensions') and provides clear guidance on what to do after creation ('use add_product_rule_tool or add_zip_rule_tool to define the combinations, then use estimate_with_binding_tool to submit'). It effectively distinguishes this from sibling tools in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the asynchronous nature of the task (returns a task ID for tracking, requires separate tools for progress/results), specifies circuit constraints ('should NOT contain measurement operations'), and outlines the return structure. However, it doesn't mention potential rate limits, authentication requirements, or error conditions beyond the status field.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, args, returns, example, note) and front-loaded with the core functionality. While comprehensive, some sentences could be more concise (e.g., the mathematical notation could be simplified). Overall, it efficiently conveys necessary information without significant waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (quantum computation with asynchronous execution), no annotations, and an output schema that documents the return structure, the description provides complete context. It covers purpose, parameters, usage flow with sibling tools, constraints, and includes a practical example. The output schema handles return value details, so the description appropriately focuses on operational guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed semantics for all three parameters. It explains the 'circuit' parameter accepts single circuits or lists in specific formats, the 'observable' parameter accepts dict or Pauli string formats with examples, and 'device_id' is clearly identified as a target device identifier. The example further clarifies usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('execute expectation estimation task'), the resource ('quantum circuit'), and the mathematical operation ('computes the expectation value ⟨ψ|O|ψ⟩'). It distinguishes from siblings like 'batch_estimate_tool' by focusing on single/batch execution rather than purely batch operations, and from 'sample_tool' by computing expectation values rather than sampling measurements.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: it names 'get_task_status_tool' to check progress and 'get_task_results_tool' to retrieve results after completion. It also implicitly distinguishes from 'batch_estimate_tool' by handling both single and batch circuits, and from 'estimate_with_binding_tool' by not requiring pre-bound observables.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it's a task submission tool (not immediate execution), returns a task ID for tracking, and requires follow-up tools to retrieve results. It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial operational context beyond basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Args, Returns, Example, Note) and front-loaded purpose. While comprehensive, some sentences could be more concise (e.g., the first two sentences convey similar information). Overall, it's efficiently organized with minimal redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (task submission with follow-up requirements), no annotations, and the presence of an output schema, the description provides complete context. It explains the workflow, parameter meanings, return structure, and references all necessary sibling tools. The output schema existence means the description doesn't need to detail return values beyond what's already provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining both parameters: 'binding_id' is identified as 'The ID returned by create_circuit_observable_binding_tool' and 'device_id' as 'Target device ID (e.g., '20')'. The description adds crucial semantic context that the schema alone doesn't provide.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('execute expectation estimation', 'submit a multi-objective expectation estimation task') and identifies the resource ('CircuitObservableBinding'). It distinguishes from siblings like 'estimate_tool' by specifying it uses a previously created binding with product/zip rules defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: it specifies prerequisites ('previously created CircuitObservableBinding with its product/zip rules defined'), references the sibling tool that creates the binding ('create_circuit_observable_binding_tool'), and explicitly names alternative tools for follow-up actions ('get_task_status_tool and get_task_results_tool to check progress and retrieve results').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively describes the tool's behavior: it's a read-only retrieval operation (implied by 'Get' and 'Retrieve'), specifies what information is returned, and provides a concrete example of usage. However, it doesn't mention potential error conditions, rate limits, or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections (description, Args, Returns, Example) and front-loaded purpose. The example is helpful but slightly verbose; every sentence earns its place by providing necessary information for tool understanding and usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, no annotations, 0% schema coverage, but presence of an output schema, the description is remarkably complete. It covers purpose, usage guidelines, parameter semantics, return structure, and provides an example - effectively compensating for all gaps in structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must fully compensate. It provides complete parameter semantics: explains what device_id represents, gives an example value ('20'), and explicitly references the sibling tool to find valid values. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Get detailed properties') and resource ('specific QPU device'), distinguishing it from siblings like list_qpu_devices_tool. It uses precise terminology like 'quantum computing device' and 'comprehensive information' to define scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided on when to use this tool: for retrieving properties of a specific device, with a direct alternative named (list_qpu_devices_tool to find device IDs). The description clearly indicates this is for detailed information after device selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it submits circuits for asynchronous execution, returns a task ID for tracking, requires follow-up tools to get results, and explains the sampling process (multiple executions to get measurement distribution). It mentions statistical accuracy implications of the shots parameter. It doesn't cover rate limits or authentication requirements, but provides substantial operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Args, Returns, Example, Note) and front-loaded purpose. Most sentences earn their place by providing essential information. It could be slightly more concise in the circuit format explanation, but overall efficiently conveys complex information without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (quantum computing task with 4 parameters, no annotations, but with output schema), the description is remarkably complete. It covers purpose, parameters, return values, usage workflow, examples, and integration with sibling tools. The output schema exists, so the description appropriately focuses on operational context rather than duplicating return structure details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics. It explains each parameter's purpose, format options, and constraints: 'circuit' accepts multiple formats with examples, 'device_id' references how to find available devices, 'shots' explains default and effect on accuracy, and 'measure_qubits' clarifies optional usage. The description adds significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('execute quantum circuit sampling task', 'submit a quantum circuit for sampling') and identifies the resource (QPU device). It distinguishes from sibling tools like 'batch_sample_tool' by focusing on single/batch circuit sampling rather than batch-specific operations, and from 'estimate_tool' by specifying sampling vs. estimation tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: for quantum circuit sampling on QPU devices. It names alternatives for related tasks: 'list_qpu_devices_tool' to find available devices, 'get_task_status_tool' to check progress, and 'get_task_results_tool' to retrieve results. It also distinguishes from batch operations by handling both single and batch circuits.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well. It explains the asynchronous nature (returns task_id, mentions companion status/result tools), specifies format requirements ('Circuits should NOT contain measurement operations'), and describes the return structure. It doesn't mention rate limits, authentication needs, or error conditions, keeping it from a perfect score.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured with clear sections: purpose statement, parameter explanations, return documentation, example, and usage notes. Every sentence adds value without redundancy. The information is front-loaded with the core purpose immediately stated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (batch quantum computation), no annotations, and 0% schema coverage, the description provides excellent completeness. It covers purpose, parameters, returns, example usage, and follow-up actions. The output schema exists, so the description appropriately focuses on explaining the semantics rather than repeating structural details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics. It explains what 'circuits' should contain (formats, exclusion of measurements), shows 'observable' format options with examples, and clarifies 'device_id' purpose. The example further illustrates proper usage of all three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Execute batch expectation estimation'), resource ('with a single observable'), and pattern ('n circuits + 1 observable'). It distinguishes this tool from potential siblings by explaining this is for comparing different circuit strategies against the same metric, unlike single-circuit estimation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'useful for comparing different circuit strategies against the same metric' tells when to use it. It also distinguishes from alternatives by noting this is the 'n circuits + 1 observable' pattern. The Note section explicitly mentions companion tools for checking progress and retrieving results.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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