mcp-pack 0.3.0

Package for accessing package artifacts via MCP

MCP Pack

A tool for creating and managing documentation databases from GitHub repositories.

Quickstart to create a database

Start the Qdrant server:

docker compose up

With a Qdrant server running:

uvx mcp_pack create_db https://github.com/user/repo

To start a server for querying the documentation:

mcp_pack create_server --module-name your_module_name

see the examples/ folder for how to setup the MCP server.

Installation

# Install from pip
pip install mcp_pack

Prerequisites

• Qdrant server running (by default at http://localhost:6333)
• GitHub token (optional, but recommended to avoid rate limits)
• OpenAI API key (optional, for summarizing Jupyter notebooks)

Usage

See example/ folder.

Create a documentation database

# Basic usage
mcp_pack create_db https://github.com/user/repo

# With @ prefix syntax
mcp_pack create_db @https://github.com/user/repo

# With additional options
mcp_pack create_db @https://github.com/user/repo \
    --output-dir ./output \
    --verbose \
    --include-notebooks \
    --include-rst \
    --github-token YOUR_GITHUB_TOKEN \
    --openai-api_key YOUR_OPENAI_API_KEY

Clean the database

# Delete all collections
mcp_pack clean_db

# Delete a specific collection
mcp_pack clean_db --collection repo-name

List Database Collections

To list all collections in the Qdrant database, use the following command:

# Use default Qdrant
mcp_pack list_db

# Use Custom Qdrant server URL 
mcp_pack list_db --qdrant-url http://localhost:6333

This will display all the collections currently stored in the Qdrant database.

Create and Run a Query Server

To create and run a server for querying module documentation:

# Basic usage with default settings
mcp_pack create_server --module-name your_module_name

# With custom settings
mcp_pack create_server --module-name your_module_name \
    --transport sse \
    --port 8080 \
    --qdrant-url http://your-qdrant-server:6333 \
    --encoder-model all-MiniLM-L6-v2 \
    --collection-name custom_collection

This will start a server that provides semantic search capabilities over your module's documentation.

Environment Variables

You can set environment variables instead of passing command-line arguments:

# Create a .env file
GITHUB_TOKEN=your_github_token
OPENAI_API_KEY=your_openai_api_key

Options

create_db

• repo_url: GitHub repository URL (can be prefixed with @)
• --output-dir, -o: Directory to save JSONL output
• --verbose, -v: Verbose output
• --include-notebooks: Include Jupyter notebooks
• --include-rst: Include RST files
• --exclude-tests: Exclude test files and directories
• --module-name: Name of the module (defaults to repository name)
• --db-path: Path to store the database
• --qdrant-url: Qdrant server URL (default: http://localhost:6333)
• --github-token: GitHub personal access token
• --openai-api-key: OpenAI API key

clean_db

• --qdrant-url: Qdrant server URL (default: http://localhost:6333)
• --collection: Specific collection to delete (optional)

list_db

• --qdrant-url: Qdrant server URL (default: http://localhost:6333)

create_server

• --module-name: Name of the module to query (required)
• --qdrant-url: Qdrant server URL (default: http://localhost:6333)
• --encoder-model: SentenceTransformer model to use (default: all-MiniLM-L6-v2)
• --collection-name: Name of the Qdrant collection (defaults to module_name)
• --transport: Transport method for the MCP server (default: stdio, choices: stdio, sse)
• --port: Port number for the MCP server (default: 8000)

Global Options

• --version: Show version information

Additional info

> python -m mcp_pack.create_db --help 
Create documentation database for a GitHub repository

positional arguments:
  repo_url              GitHub repository URL

options:
  -h, --help            show this help message and exit
  --output-dir OUTPUT_DIR, -o OUTPUT_DIR
                        Directory to save JSONL output
  --verbose, -v         Verbose output
  --include-notebooks   Include Jupyter notebooks
  --include-rst         Include rst files
  --db-path DB_PATH     Path to store the database
  --qdrant-url QDRANT_URL
                        Qdrant server URL
  --github-token GITHUB_TOKEN
                        GitHub personal access token
  --openai-api-key OPENAI_API_KEY
                        OpenAI API key

python -m mcp_pack.clean_db --help
Clean Qdrant database collections

options:
  -h, --help            show this help message and exit
  --qdrant-url QDRANT_URL
                        Qdrant server URL
  --collection COLLECTION
                        Specific collection to delete (optional, if not provided, all collections will be deleted)

Resources

• Qdrant quickstart
• Qdrant API