Drf Mcp Docs

drf-mcp-docs

API documentation via MCP for AI coding agents

---

drf-mcp-docs exposes your Django REST Framework API documentation through the Model Context Protocol (MCP) so AI coding agents can read, understand, and help you write correct frontend integration code.

> How is this different from other Django+MCP packages? > > Packages like django-mcp-server and django-rest-framework-mcp expose DRF actions as MCP tools — the AI agent calls your endpoints directly. drf-mcp-docs is fundamentally different: it exposes API documentation so AI agents can help developers write frontend code (React, Vue, Angular, etc.). > > Think of it as: drf-spectacular generates docs for humans in a browser → drf-mcp-docs generates docs for AI agents via MCP.

Features

• MCP Resources — Browse your API structure: overview, endpoints, schemas, auth methods
• MCP Tools — Search endpoints, get detailed docs, generate request/response examples
• Code Generation — Generate integration code with real types and docs (JS/TS: fetch, axios, ky — Python: requests, httpx — cURL)
• Multi-adapter — Works with drf-spectacular, drf-yasg, or DRF's built-in schema generation
• Zero risk — Read-only documentation exposure, no data mutation possible
• Two transports — stdio for local AI tools, streamable-http for remote/network access

Quick Start

1. Install

pip install drf-mcp-docs

With a specific schema generator:

pip install drf-mcp-docs[spectacular]   # recommended
pip install drf-mcp-docs[yasg]

2. Configure

Add to your Django settings:

INSTALLED_APPS = [
    # ...
    'rest_framework',
    'drf_mcp_docs',
]

That's it for basic usage. drf-mcp-docs auto-detects your schema generator.

3. Run

stdio transport (for local AI tools like Claude Code, Cursor, etc.):

python manage.py runmcpserver --transport stdio

Streamable HTTP transport (for network access):

python manage.py runmcpserver --transport streamable-http --host 0.0.0.0 --port 8100

Check configuration (validate settings, adapter, and schema):

python manage.py checkmcpconfig

4. Connect your AI tool

Claude Code (~/.claude.json):

{
  "mcpServers": {
    "my-api-docs": {
      "command": "python",
      "args": ["manage.py", "runmcpserver", "--transport", "stdio"],
      "cwd": "/path/to/your/django/project"
    }
  }
}

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "my-api-docs": {
      "command": "python",
      "args": ["manage.py", "runmcpserver", "--transport", "stdio"],
      "cwd": "/path/to/your/django/project"
    }
  }
}

What AI Agents Can Do

Once connected, your AI coding agent can:

You: "Show me all the product endpoints"
Agent: [reads api://endpoints resource, filters by tag]

You: "Generate a React hook to create a new product"
Agent: [calls get_endpoint_detail for POST /api/products/]
       [calls get_request_example for the request body]
       [calls generate_code_snippet with typescript + fetch]
       → Generates a complete, typed React hook with correct fields

Available Resources

| Resource URI | Description | |---|---| | api://overview | API title, version, base URL, auth summary, tags, endpoint count | | api://endpoints | All endpoints: path, method, summary, tags (compact listing) | | api://endpoints/{method}/{path} | Full detail for one endpoint | | api://schemas | All schema/model definitions (names + field summaries) | | api://schemas/{name} | Full schema with properties, types, constraints | | api://auth | Authentication guide with all auth methods |

Available Tools

| Tool | Parameters | Description | |---|---|---| | search_endpoints | query, method?, tag? | Search endpoints by keyword | | get_endpoint_detail | path, method | Full endpoint documentation | | get_request_example | path, method | Example request body and parameters | | get_response_example | path, method, status_code? | Example response | | generate_code_snippet | path, method, language?, client? | Integration code (JS/TS, Python, cURL) with pagination support | | list_schemas | — | All data model names and descriptions | | get_schema_detail | name | Full schema with all fields and types |

Configuration

All settings are optional. Add a DRF_MCP_DOCS dict to your Django settings:

DRF_MCP_DOCS = {
    # Server
    'SERVER_NAME': 'my-api',                    # MCP server name (default: 'drf-mcp-docs')
    'SERVER_INSTRUCTIONS': 'Custom prompt...',   # Instructions shown to AI agents

    # Schema
    'SCHEMA_ADAPTER': None,                      # Auto-detect, or full dotted path
    'SCHEMA_PATH_PREFIX': '/api/',               # Only include endpoints under this prefix
    'EXCLUDE_PATHS': ['/api/internal/'],          # Paths to exclude
    'CACHE_SCHEMA': not DEBUG,                   # Cache in production, refresh in dev
    'CACHE_TTL': None,                           # Schema cache TTL in seconds (None = no expiry)

    # Transport
    'TRANSPORT': 'streamable-http',              # Default transport: 'streamable-http' or 'stdio'
    'MCP_ENDPOINT': '/mcp/',                     # URL path for HTTP transport

    # Code generation
    'DEFAULT_CODE_LANGUAGE': 'javascript',       # 'javascript', 'typescript', or 'python'
    'DEFAULT_HTTP_CLIENT': 'fetch',              # 'fetch', 'axios', 'ky', 'requests', or 'httpx'
}

Schema Adapter Selection

drf-mcp-docs auto-detects your schema generator in this priority order:

1. drf-spectacular (recommended) — most complete OpenAPI 3.x output
2. drf-yasg — Swagger 2.0 auto-converted to OpenAPI 3.0
3. DRF built-in — basic fallback with limited schema detail

To force a specific adapter:

DRF_MCP_DOCS = {
    'SCHEMA_ADAPTER': 'drf_mcp_docs.adapters.spectacular.SpectacularAdapter',
}

ASGI Integration

For projects using ASGI, mount the MCP endpoint alongside your Django app:

# asgi.py
from django.core.asgi import get_asgi_application
from drf_mcp_docs.urls import mount_mcp

django_app = get_asgi_application()
application = mount_mcp(django_app)  # Mounts at /mcp/ by default

With custom path:

application = mount_mcp(django_app, path="/api-docs-mcp/")

Development

git clone https://github.com/Abdulkhalek-1/drf-mcp-docs.git
cd drf-mcp-docs
pip install -e ".[dev]"
pytest

How It Works

┌─────────────┐     ┌──────────────┐     ┌─────────────────┐     ┌───────────┐
│  AI Agent   │────>│  MCP Server  │────>│ Schema Processor │────>│  Adapter  │
│ (Claude,    │<────│  (FastMCP)   │<────│  (OpenAPI dict   │<────│ (spectac- │
│  Cursor...) │     │              │     │   → structured)  │     │  ular,    │
└─────────────┘     └──────────────┘     └─────────────────┘     │  yasg,    │
                    Resources + Tools     Dataclasses + Search    │  DRF)     │
                                                                  └───────────┘

1. Adapter pulls the OpenAPI schema from your chosen generator
2. Processor transforms the raw dict into structured, AI-friendly dataclasses
3. MCP Server exposes resources (browsable docs) and tools (search, examples, code gen)
4. AI Agent connects via stdio or HTTP, reads your API docs, helps write frontend code

License

MIT License. See [LICENSE](LICENSE) for details.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Abdulkhalek-1
• Source: Abdulkhalek-1/drf-mcp-docs
• License: MIT
• Homepage: http://abdulkhalek-1.github.io/drf-mcp-docs/

Install and usage instructions live in the source repository linked above.