cloudwright-mcp
The Cloudwright MCP server lets you design, cost, validate, and export cloud architectures using natural language across AWS, GCP, Azure, and Databricks.
Design & Modification
design_architecture— Generate a complete architecture spec from a plain-English descriptionmodify_architecture— Evolve an existing spec with natural-language instructions (e.g. "add a Redis cache")compare_providers— Translate an architecture across cloud providers to see equivalent service mappings
Cost Estimation
estimate_cost— Per-component monthly cost breakdown (offline)compare_provider_costs— Compare costs for the same architecture across providers (offline)
Compliance & Security
validate_compliance— Check against HIPAA, PCI-DSS, SOC 2, FedRAMP, GDPR, and AWS Well-Architectedsecurity_scan— Detect anti-patterns (unencrypted stores, public databases, missing WAF, etc.)scan_terraform— Audit existing Terraform HCL for security misconfigurationslint_architecture— Check for hygiene issues, anti-patterns, and best-practice violations
Analysis
analyze_blast_radius— Identify failure domains, SPOFs, and transitive component impactscore_architecture— Overall quality score (0–100) across reliability, security, cost, compliance, and complexitydiff_architectures— Compare two architecture versions with cost delta and compliance impact
Export
export_architecture— Export to Terraform, CloudFormation, Mermaid, D2, SBOM (CycloneDX), AI BOM, or compliance audit report
Discovery
list_services— List supported cloud services by providercatalog_search— Search instance catalog by vCPU, memory, price, or free-text for right-sizing
Multi-turn Chat Sessions
chat_create_session/chat_send— Stateful, context-aware architecture design conversations with optional provider, compliance, and budget constraintschat_list_sessions/chat_delete_session— Manage saved sessions
Provides Terraform exporter module for Databricks infrastructure as part of the multi-cloud infrastructure code generation capabilities.
Supports containerized deployment of the web server with Dockerfile and docker-compose.yml for production-ready containerization.
Supports Kubernetes service normalization across cloud providers (AWS EKS, GCP GKE, Azure AKS) in architecture specifications.
Supports MongoDB service normalization across cloud providers (AWS DocumentDB, GCP Cloud Datastore, Azure Cosmos DB) in architecture specifications.
Provides LLM capabilities for architecture design, modification, chat interactions, and ADR generation through OpenAI models like GPT-5, with provider-aware routing and API key configuration.
Supports PostgreSQL service normalization across cloud providers (AWS RDS, GCP Cloud SQL, Azure Database for PostgreSQL) in architecture specifications.
Supports Redis service normalization across cloud providers (AWS ElastiCache, GCP Memorystore, Azure Cache for Redis) in architecture specifications.
Generates Terraform infrastructure as code from architecture specifications, with provider-specific modules for AWS, GCP, Azure, and Databricks.
Cloudwright
Describe a cloud architecture in English. Get Terraform, costs, and a compliance check.
pip install 'cloudwright-ai[cli]'
export ANTHROPIC_API_KEY=sk-ant-...
cloudwright design "HIPAA healthcare API on AWS with Postgres and Redis"Cloudwright turns one line of English into a typed spec, a cost breakdown, a control-mapped compliance report,
and infrastructure code. It covers AWS, GCP, Azure and Databricks across 114 service keys. Only design,
modify, chat and adr call a model. Every other command runs offline and needs no API key.
Quickstart · Compliance · Agents · Docs · Changelog
A prompt produces a spec, a cost, a control-mapped report, and Terraform
Spec. Typed YAML you commit, diff and review. Everything below reads from it.
Cost. Per component and region-aware, with a confidence flag on every line.
Compliance. HIPAA, SOC 2, PCI-DSS, FedRAMP, GDPR, ISO 27001 and NIST 800-53 control IDs.
Infrastructure code. Terraform, OpenTofu, Pulumi (TypeScript or Python) and CloudFormation.
Diagrams. ASCII, Mermaid, D2, and a web canvas you can edit by hand.
An MCP server, so any coding agent runs the same checks inside its own loop.
Exports carry safe defaults. S3 gets a public-access block, SSE and versioning. RDS gets encryption, multi-AZ and deletion protection. EC2 gets IMDSv2. A compliance framework overrides the workload profile, and always forces encryption and high availability.
Related MCP server: insideout-mcp
Quickstart
cloudwright design "HIPAA healthcare API on AWS with Postgres and Redis"
cloudwright cost spec.yaml --workload-profile medium
cloudwright compliance spec.yaml --frameworks hipaa,soc2
cloudwright export spec.yaml --format terraform -o ./infra
cloudwright plan spec.yaml --target terraform # proves it deploys, never applies
cloudwright chat --web # canvas at http://localhost:8765Add --json before any subcommand for machine-readable output, or --stream to watch tokens arrive. Set ANTHROPIC_API_KEY or OPENAI_API_KEY for the four commands that need a model.
Every finding carries the control ID it violates
Other tools scan infrastructure after you deploy it. Cloudwright maps each finding to its control before any
resource exists. The fix then costs a spec edit, not a change ticket. HIPAA 164.312(a)(2)(iv), SOC 2 CC6.1
and FedRAMP SC-28 come from the built-in scanner, with no extra tooling. Checkov folds into the same report
when it sits on your PATH.
--oscalwrites an OSCAL 1.1.2 component-definition with deterministic UUIDs.--traceabilityprints the chain from component to resource to control to status.cloudwright planrunsterraform validateagainst the export, and never applies.
The review needs no API key and no network
cloudwright review runs the scorer, the linter and the validator over a spec, and returns one severity-ranked
report. The same three critics run inside cloudwright design. When blocking findings survive generation, the
architect repairs the spec once and records the change in spec.metadata.critique. Pass
Architect(repair=False) to turn that off.
Canvas edits never call the model, so they are instant and free
Add, drag, connect, edit and delete are deterministic frontend mutations. The Catalog drawer serves the resource
list per provider and five approved multi-resource modules. Its standards check flags orphan connections,
partial modules and missing tags. An intact module exports as a single Terraform module block, with the
catalog's pinned source and version.
One MCP server reaches 11 coding agents
cloudwright integrate --harness claude-code # exact wiring, in that client's format
cloudwright integrate --harness cursor --write # merge it into the right file
cloudwright integrate --rules --agent-file claude # a gate block for CLAUDE.mdDo not hand-write the config. cloudwright integrate emits it for Claude Code, Cursor, Cline, Windsurf, GitHub
Copilot, Zed, Codex CLI, Junie, Kiro and Antigravity. Aider gets a CLI-pipe recipe instead, because it speaks
no MCP. Every client wants a different shape: Zed wants context_servers, Copilot wants servers, and Codex
wants a TOML table.
The server exposes 22 tools in 9 groups: design, cost, validate, analyze, export, session, review, compliance and plan. Full matrix in docs/integrations.md.
Nine offline commands grade, scan and compare a spec
lint runs 10 anti-pattern checks. score grades 5 dimensions. analyze reports blast radius and single
points of failure. policy enforces policy-as-code with 9 built-in rules. security scans the spec and the
exported HCL. drift compares a design against a tfstate, and --remediate turns the gap into a cost,
compliance and plan preview.
review, compliance and plan are above. See docs/cli-reference.md.
Python API
from cloudwright import ArchSpec
from cloudwright.cost import CostEngine
from cloudwright.validator import Validator
from cloudwright.exporter import export_spec
spec = ArchSpec.from_file("spec.yaml")
priced = CostEngine().estimate(spec, workload_profile="medium")
findings = Validator().validate(spec, compliance=["hipaa", "pci-dss"])
hcl = export_spec(spec, "terraform", output_dir="./infra")v1.9.0 gave the diagram arrowheads and got the canvas off React Flow's defaults
Measured by driving all 16 init templates through the running app, and 8 interactions at 1920, 1440 and 390px.
Every connection draws an arrowhead. The computed
markerEndwasnoneon every edge.Connection lines clear 3:1 contrast. They ran at 1.42:1 in light and 1.81:1 in dark.
The canvas fits a phone. The 0.5 zoom floor left 2 of 8 nodes off the pane at 390px; the floor is 0.12.
A drag inside a VPC pans the canvas. It used to drag the box out of shape and save nothing.
A node moves the distance you drag it. A tight boundary plus
extent: "parent"allowed 5 to 10px.Connections stop hiding behind cards. 17 of them did across the 16 templates; 5 still do.
Earlier releases added control-ID mapping and plan (v1.5.0), the self-correcting architect and OSCAL (v1.6.0), cloudwright integrate (v1.7.0), and the dark theme and responsive layout (v1.8.0). Full history in CHANGELOG.md.
Compatibility
Python 3.12+
Models: Anthropic (Claude Sonnet, Haiku) and OpenAI (GPT-5+ family), auto-detected from env.
Clouds: AWS, GCP, Azure, Databricks. 114 service keys total.
Install variants:
cloudwright-ai[cli],cloudwright-ai[web],cloudwright-ai-mcp.
Contributing, license, changelog
Contributing guide: CONTRIBUTING.md
License: MIT, see LICENSE
Full release history: CHANGELOG.md
Maintenance
Resources
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