Deepwiki MCP Server

⚠️ IMPORTANT NOTICE: This server is currently not working since DeepWiki has cut off the possibility to scrape it. We recommend using the official DeepWiki MCP server at https://docs.devin.ai/work-with-devin/deepwiki-mcp for the time being.

This is an unofficial Deepwiki MCP Server

It takes a Deepwiki URL via MCP, crawls all relevant pages, converts them to Markdown, and returns either one document or a list by page.

Features

• 🔒 Domain Safety: Only processes URLs from deepwiki.com
• 🧹 HTML Sanitization: Strips headers, footers, navigation, scripts, and ads
• 🔗 Link Rewriting: Adjusts links to work in Markdown
• 📄 Multiple Output Formats: Get one document or structured pages
• 🚀 Performance: Fast crawling with adjustable concurrency and depth
• NLP: It's to search just for the library name

Usage

Prompts you can use:

deepwiki fetch how can i use gpt-image-1 with "vercel ai" sdk

deepwiki fetch how can i create new blocks in shadcn?

deepwiki fetch i want to understand how X works

Fetch complete Documentation (Default)

use deepwiki https://deepwiki.com/shadcn-ui/ui
use deepwiki multiple pages https://deepwiki.com/shadcn-ui/ui

Single Page

use deepwiki fetch single page https://deepwiki.com/tailwindlabs/tailwindcss/2.2-theme-system

Get by shortform

use deepwiki fetch tailwindlabs/tailwindcss

deepwiki fetch library

deepwiki fetch url
deepwiki fetch <name>/<repo>

deepwiki multiple pages ...
deepwiki single page url ...

Cursor

Add this to .cursor/mcp.json file.

{
  "mcpServers": {
    "mcp-deepwiki": {
      "command": "npx",
      "args": ["-y", "mcp-deepwiki@latest"]
    }
  }
}

MCP Tool Integration

The package registers a tool named deepwiki_fetch that you can use with any MCP-compatible client:

{
  "action": "deepwiki_fetch",
  "params": {
    "url": "https://deepwiki.com/user/repo",
    "mode": "aggregate",
    "maxDepth": "1"
  }
}

Parameters

• url (required): The starting URL of the Deepwiki repository
• mode (optional): Output mode, either "aggregate" for a single Markdown document (default) or "pages" for structured page data
• maxDepth (optional): Maximum depth of pages to crawl (default: 10)

Response Format

Success Response (Aggregate Mode)

{
  "status": "ok",
  "data": "# Page Title

Page content...

---

# Another Page

More content...",
  "totalPages": 5,
  "totalBytes": 25000,
  "elapsedMs": 1200
}

Success Response (Pages Mode)

{
  "status": "ok",
  "data": [
    {
      "path": "index",
      "markdown": "# Home Page

Welcome to the repository."
    },
    {
      "path": "section/page1",
      "markdown": "# First Page

This is the first page content."
    }
  ],
  "totalPages": 2,
  "totalBytes": 12000,
  "elapsedMs": 800
}

Error Response

{
  "status": "error",
  "code": "DOMAIN_NOT_ALLOWED",
  "message": "Only deepwiki.com domains are allowed"
}

Partial Success Response

{
  "status": "partial",
  "data": "# Page Title

Page content...",
  "errors": [
    {
      "url": "https://deepwiki.com/user/repo/page2",
      "reason": "HTTP error: 404"
    }
  ],
  "totalPages": 1,
  "totalBytes": 5000,
  "elapsedMs": 950
}

Progress Events

When using the tool, you'll receive progress events during crawling:

Fetched https://deepwiki.com/user/repo: 12500 bytes in 450ms (status: 200)
Fetched https://deepwiki.com/user/repo/page1: 8750 bytes in 320ms (status: 200)
Fetched https://deepwiki.com/user/repo/page2: 6200 bytes in 280ms (status: 200)

Local Development - Installation

Local Usage

{
  "mcpServers": {
    "mcp-deepwiki": {
      "command": "node",
      "args": ["./bin/cli.mjs"]
    }
  }
}

From Source

# Clone the repository
git clone https://github.com/regenrek/deepwiki-mcp.git
cd deepwiki-mcp

# Install dependencies
npm install

# Build the package
npm run build

Direct API Calls

For HTTP transport, you can make direct API calls:

curl -X POST http://localhost:3000/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "id": "req-1",
    "action": "deepwiki_fetch",
    "params": {
      "url": "https://deepwiki.com/user/repo",
      "mode": "aggregate"
    }
  }'

Configuration

Environment Variables

• DEEPWIKI_MAX_CONCURRENCY: Maximum concurrent requests (default: 5)
• DEEPWIKI_REQUEST_TIMEOUT: Request timeout in milliseconds (default: 30000)
• DEEPWIKI_MAX_RETRIES: Maximum retry attempts for failed requests (default: 3)
• DEEPWIKI_RETRY_DELAY: Base delay for retry backoff in milliseconds (default: 250)

To configure these, create a .env file in the project root:

DEEPWIKI_MAX_CONCURRENCY=10
DEEPWIKI_REQUEST_TIMEOUT=60000
DEEPWIKI_MAX_RETRIES=5
DEEPWIKI_RETRY_DELAY=500

Docker Deployment (Untested)

Build and run the Docker image:

# Build the image
docker build -t mcp-deepwiki .

# Run with stdio transport (for development)
docker run -it --rm mcp-deepwiki

# Run with HTTP transport (for production)
docker run -d -p 3000:3000 mcp-deepwiki --http --port 3000

# Run with environment variables
docker run -d -p 3000:3000 \
  -e DEEPWIKI_MAX_CONCURRENCY=10 \
  -e DEEPWIKI_REQUEST_TIMEOUT=60000 \
  mcp-deepwiki --http --port 3000

Development

# Install dependencies
pnpm install

# Run in development mode with stdio
pnpm run dev-stdio

# Run tests
pnpm test

# Run linter
pnpm run lint

# Build the package
pnpm run build

Troubleshooting

Common Issues

1. Permission Denied: If you get EACCES errors when running the CLI, make sure to make the binary executable:

   chmod +x ./node_modules/.bin/mcp-deepwiki
   

2. Connection Refused: Make sure the port is available and not blocked by a firewall:

   # Check if port is in use
   lsof -i :3000
   

3. Timeout Errors: For large repositories, consider increasing the timeout and concurrency:

   DEEPWIKI_REQUEST_TIMEOUT=60000 DEEPWIKI_MAX_CONCURRENCY=10 npx mcp-deepwiki
   

Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

License

MIT

Links

• X/Twitter: @kregenrek
• Bluesky: @kevinkern.dev

Courses

• Learn Cursor AI: Ultimate Cursor Course
• Learn to build software with AI: instructa.ai

See my other projects:

• AI Prompts - Curated AI Prompts for Cursor AI, Cline, Windsurf and Github Copilot
• codefetch - Turn code into Markdown for LLMs with one simple terminal command
• aidex A CLI tool that provides detailed information about AI language models, helping developers choose the right model for their needs.# tool-starter