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Architecture Pattern MCP

Designs and evaluates software architectures from requirements using 40 built-in patterns and structured MCP tools.

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Install Architecture Pattern MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "architecture-pattern-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "fallback"
      ],
      "env": {
        "GENERATOR_API_KEY": "YOUR_VALUE_HERE",
        "GENERATOR_PROVIDER": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y fallback

Process exited before answering initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 7d ago.

Overview

The architecture-pattern-mcp MCP server analyzes requirements and domains, selects suitable architecture patterns, and produces structured designs for AI coding agents. It exposes tools for analysis, generation, evaluation, pattern discovery, and asynchronous design jobs. Results can include components, relationships, API contracts, data models, event contracts, quality scores, and recommendations. Reach for it when an agent needs a documented architecture proposal rather than only code-level assistance.

Use cases

  • Analyze requirements and select architecture patterns
  • Generate a structured design for a new system
  • Evaluate an architecture against quality attributes
  • Explore the built-in architecture pattern catalog
  • Run long architecture jobs asynchronously

Key features

  • 40 built-in architecture patterns
  • Structured architecture generation
  • Quality attribute evaluation
  • Pattern catalog search and retrieval
  • Asynchronous design jobs
  • stdio and streamable HTTP transports

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

7/7 checks healthy over the last 41d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 13d ago via OSV.dev · fallback (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1Stargazers on the source repository.
npm downloads195Package downloads in the last 30 days.
Last commit12d agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How Architecture Pattern MCP works

What architecture-pattern-mcp does

The architecture-pattern-mcp MCP server turns a requirements description and domain into an architecture proposal. It considers 40 built-in patterns, then can generate a design containing components, relationships, API contracts, data models, and event contracts. The server also evaluates designs against maintainability, scalability, reliability, security, and performance.

It is intended for AI coding agents that need help deciding how a system should be structured. Example domains include data processing, microservices, and e-commerce. A request can describe a concrete workload, such as an event pipeline with Kafka, Redis, InfluxDB, and S3, and the resulting design can include quality scores and recommendations.

How it works

The architecture-pattern-mcp MCP server accepts free-text requirements and a domain for its design-oriented tools. analyze_architecture examines the problem and identifies matching patterns. generate_architecture creates a design from the requirements and optionally supplied selected_patterns. evaluate_architecture accepts an architecture design and criteria such as scalability or reliability.

For a complete workflow, design_architecture combines the design process and evaluation. Long-running work can use submit_architecture_design_job, which returns a job ID, followed by repeated calls to get_architecture_design_status. Status values include pending, running, completed, failed, and cancelled; a completed job contains the design result.

The server supports streamable HTTP at /mcp and can also run as a stdio subprocess. The repository includes guidance for Claude Code, OpenCode, and Codex CLI. Its OMP skill adds workflow and timeout guidance, but the tool schemas are client-agnostic.

Tools and capabilities

The architecture-pattern-mcp MCP server provides tools to:

  • Analyze requirements and identify relevant architecture patterns.
  • Generate a concrete architecture from requirements and selected patterns.
  • Evaluate an existing design against chosen quality criteria.
  • Run a complete design workflow with design_architecture.
  • Submit asynchronous design jobs and poll their status.
  • List all patterns or filter them by category.
  • Retrieve the full definition of a named pattern with get_architecture_pattern.

The pattern catalog can be queried with filters such as messaging. Design results expose structured data rather than only prose, making them suitable for an agent workflow that needs to inspect components, scores, and recommendations.

Setup and configuration

The documented quickstart clones the repository, sets GENERATOR_API_KEY, and starts the Docker Compose configuration. The development Compose setup serves MCP at http://localhost:8060/mcp; the systemd setup uses port 8050. Local installation uses Python 3.12 or newer and can run the module directly or install the package with uv.

For stdio clients, run architecture-pattern-mcp --transport stdio and pass the generator credentials through the client configuration. HTTP clients connect to the running /mcp endpoint. OpenCode reads the API key from the server configuration file at ~/.config/architecture-pattern-mcp/config.json, rather than from OpenCode's environment.

Limitations and notes

The design workflow can exceed short MCP request deadlines. The repository reports measured durations of about 66 seconds for analysis alone and 197 seconds for evaluation in one OMP-related workflow. Clients with short timeouts should use the asynchronous job tools, while the one-shot workflow may require a higher server timeout.

The generator requires an API key, and the provider is configured through GENERATOR_PROVIDER in the documented client examples. Pattern selection and evaluation quality depend on the requirements, domain, supplied architecture, and criteria passed to the tools. The repository's OMP skill is specifically written for Oh My Pi and should not be treated as a requirement for other MCP clients.

Read the full README View source on GitHub

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Frequently Asked Questions about Architecture Pattern MCP

architecture-pattern-mcp is an MCP server that connects AI coding agents to architecture analysis and design tools. Its main tools analyze requirements, generate architectures, evaluate designs, list 40 built-in patterns, retrieve pattern details, and run asynchronous design jobs with status polling. Results include structured components, contracts, quality scores, and recommendations.

Install it by cloning the repository and starting its Docker Compose configuration, or install the package locally with Python 3.12+ and uv. Set GENERATOR_API_KEY before starting. For stdio clients, run architecture-pattern-mcp with the stdio transport; HTTP clients use the running /mcp endpoint.

Yes, Cursor can use the asynchronous job tools described for clients with short request timeouts. Submit a design job, save the returned job ID, and poll get_architecture_design_status until it completes. The repository specifically recommends this approach for Cursor.

Call evaluate_architecture with an architecture design and criteria such as scalability or reliability. The server evaluates quality attributes including maintainability, scalability, reliability, security, and performance, then returns scores and recommendations as part of the result.

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