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Code Churn Analysis

churn
Read-only

Identify unstable files by analyzing code churn — lines added and deleted over time. High churn signals code that is frequently rewritten, indicating risk.

Instructions

Analyze code churn — how much code is being written and then rewritten. High churn indicates instability, unclear requirements, or code that is hard to get right. A file with 500 lines added and 400 deleted in a month is a red flag. NOTE: If the server was not started inside a git repo, you MUST provide repo_path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days to look back (default: 90)
limitNoMax results to return (default: 20, max: 100)
repo_pathNoAbsolute path to the git repository to analyze. Required if Claude Code was not opened in a git repo.
path_filterNoFilter to files under this path
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark the tool as read-only and non-open-world. The description adds interpretive context about churn, but its only operational note (repo_path requirement) essentially duplicates the parameter schema. It does not disclose output format, pagination, or other behavior beyond annotations.

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 three sentences with no filler. It front-loads the purpose, provides a concrete example to clarify the concept, and ends with a practical note. Every sentence earns its place.

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?

The schema covers parameters and annotations cover safety, so the description's addition of domain context is valuable. However, with no output schema, it doesn't state what the tool returns (e.g., a ranked list of files), which would be helpful. Overall, it is adequate for the tool's apparent simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides descriptions for all 4 parameters, so the baseline is 3. The description adds no additional parameter syntax or format guidance; it only reiterates the repo_path requirement already present in the 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 identifies the tool's specific action ('Analyze code churn') and resource ('code churn'), and it explains what churn means. It does not explicitly distinguish from sibling tools, but the subject matter is sufficiently unique.

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 the tool is useful (identifying instability or hard-to-maintain code) and gives a crucial prerequisite (repo_path when the server isn't in a git repo). It does not mention alternative tools or exclusion criteria, but the context is clear.

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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