MCP Connector

Query Package Repositories for LLMs

Pacman MCP Server: AI tool for querying package indices like PyPI, npm, crates.io, Docker Hub, and Terraform Registry. Search and retrieve package info.

Works with pypinpmcrates.iodocker hubterraform registry

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91
Spark score
out of 100
Updated Apr 2025
Version 0.2.0
Models
universal

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Why it matters

Empower LLMs with the ability to query and retrieve information from diverse package repositories, including PyPI, npm, crates.io, Docker Hub, and Terraform Registry.

Outcomes

What it gets done

01

Search for packages across multiple repositories.

02

Retrieve detailed information about specific packages and Docker images.

03

Query for the latest versions of Terraform modules.

04

Integrate package querying capabilities into LLM applications.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-pacman | bash

Capabilities

Tools your agent gets

search_package

Search for packages in package indexes (PyPI, npm, crates.io, Terraform)

package_info

Retrieve detailed information about a specific package

search_docker_image

Search for Docker images in Docker Hub

docker_image_info

Retrieve detailed information about a specific Docker image

terraform_module_latest_version

Retrieve the latest version of a Terraform module

Overview

Pacman MCP Server

The Pacman MCP Server is a Model Context Protocol (MCP) server that provides AI clients with capabilities to query package indices. It enables searching for packages and retrieving detailed information from repositories such as PyPI, npm, crates.io, Docker Hub, and the Terraform Registry. Use the Pacman MCP Server when an AI agent needs to programmatically search for software packages, Docker images, or Terraform modules across multiple popular registries. It is ideal for integrating package discovery and information retrieval into AI-powered development workflows.

What it does

As an AI agent, my job is to efficiently find and retrieve information about software packages and infrastructure modules to inform development decisions or automate tasks. I need a reliable way to query various package repositories. The Pacman MCP Server allows me to do this by providing tools to search and get details for packages across PyPI, npm, crates.io, Docker Hub, and the Terraform Registry. For example, I can install and run it using:

pip install mcp-server-pacman

Or configure it in an application like Claude.app:

"mcpServers": {
  "pacman": {
    "command": "python",
    "args": ["-m", "mcp_server_pacman"]
  }
}
Source README

Pacman MCP Server

A Model Context Protocol server that provides package index querying capabilities. This server enables LLMs to search and retrieve information from package repositories like PyPI, npm, crates.io, Docker Hub, and Terraform Registry.

mcp-server-pacman MCP server

Available Tools

  • search_package - Search for packages in package indices

    • index (string, required): Package index to search ("pypi", "npm", "crates", "terraform")
    • query (string, required): Package name or search query
    • limit (integer, optional): Maximum number of results to return (default: 5, max: 50)
  • package_info - Get detailed information about a specific package

    • index (string, required): Package index to query ("pypi", "npm", "crates", "terraform")
    • name (string, required): Package name
    • version (string, optional): Specific version to get info for (default: latest)
  • search_docker_image - Search for Docker images in Docker Hub

    • query (string, required): Image name or search query
    • limit (integer, optional): Maximum number of results to return (default: 5, max: 50)
  • docker_image_info - Get detailed information about a specific Docker image

    • name (string, required): Image name (e.g., user/repo or library/repo)
    • tag (string, optional): Specific image tag (default: latest)
  • terraform_module_latest_version - Get the latest version of a Terraform module

    • name (string, required): Module name (format: namespace/name/provider)

Prompts

  • search_pypi

    • Search for Python packages on PyPI
    • Arguments:
      • query (string, required): Package name or search query
  • pypi_info

    • Get information about a specific Python package
    • Arguments:
      • name (string, required): Package name
      • version (string, optional): Specific version
  • search_npm

    • Search for JavaScript packages on npm
    • Arguments:
      • query (string, required): Package name or search query
  • npm_info

    • Get information about a specific JavaScript package
    • Arguments:
      • name (string, required): Package name
      • version (string, optional): Specific version
  • search_crates

    • Search for Rust packages on crates.io
    • Arguments:
      • query (string, required): Package name or search query
  • crates_info

    • Get information about a specific Rust package
    • Arguments:
      • name (string, required): Package name
      • version (string, optional): Specific version
  • search_docker

    • Search for Docker images on Docker Hub
    • Arguments:
      • query (string, required): Image name or search query
  • docker_info

    • Get information about a specific Docker image
    • Arguments:
      • name (string, required): Image name (e.g., user/repo)
      • tag (string, optional): Specific tag
  • search_terraform

    • Search for Terraform modules in the Terraform Registry
    • Arguments:
      • query (string, required): Module name or search query
  • terraform_info

    • Get information about a specific Terraform module
    • Arguments:
      • name (string, required): Module name (format: namespace/name/provider)
  • terraform_latest_version

    • Get the latest version of a specific Terraform module
    • Arguments:
      • name (string, required): Module name (format: namespace/name/provider)

Installation

Using uv (recommended)

When using uv no specific installation is needed. We will
use uvx to directly run mcp-server-pacman.

Using PIP

Alternatively you can install mcp-server-pacman via pip:

pip install mcp-server-pacman

After installation, you can run it as a script using:

python -m mcp_server_pacman

Using Docker

You can also use the Docker image:

docker pull oborchers/mcp-server-pacman:latest
docker run -i --rm oborchers/mcp-server-pacman

Configuration

Configure for Claude.app

Add to your Claude settings:

Using uvx
"mcpServers": {
  "pacman": {
    "command": "uvx",
    "args": ["mcp-server-pacman"]
  }
}
Using docker
"mcpServers": {
  "pacman": {
    "command": "docker",
    "args": ["run", "-i", "--rm", "oborchers/mcp-server-pacman:latest"]
  }
}
Using pip installation
"mcpServers": {
  "pacman": {
    "command": "python",
    "args": ["-m", "mcp-server-pacman"]
  }
}

Configure for VS Code

For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.

Note that the mcp key is needed when using the mcp.json file.

Using uvx
{
  "mcp": {
    "servers": {
      "pacman": {
        "command": "uvx",
        "args": ["mcp-server-pacman"]
      }
    }
  }
}
Using Docker
{
  "mcp": {
    "servers": {
      "pacman": {
        "command": "docker",
        "args": ["run", "-i", "--rm", "oborchers/mcp-server-pacman:latest"]
      }
    }
  }
}

Customization - User-agent

By default, the server will use the user-agent:

ModelContextProtocol/1.0 Pacman (+https://github.com/modelcontextprotocol/servers)

This can be customized by adding the argument --user-agent=YourUserAgent to the args list in the configuration.

Development

Running Tests

  • Run all tests:

    uv run pytest -xvs
    
  • Run specific test categories:

    # Run all provider tests
    uv run pytest -xvs tests/providers/
    
    # Run integration tests for a specific provider
    uv run pytest -xvs tests/integration/test_pypi_integration.py
    
    # Run specific test class
    uv run pytest -xvs tests/providers/test_npm.py::TestNPMFunctions
    
    # Run a specific test method
    uv run pytest -xvs tests/providers/test_pypi.py::TestPyPIFunctions::test_search_pypi_success
    
  • Check code style:

    uv run ruff check .
    uv run ruff format --check .
    
  • Format code:

    uv run ruff format .
    

Debugging

You can use the MCP inspector to debug the server. For uvx installations:

npx @modelcontextprotocol/inspector uvx mcp-server-pacman

Or if you've installed the package in a specific directory or are developing on it:

cd path/to/pacman
npx @modelcontextprotocol/inspector uv run mcp-server-pacman

Release Process

The project uses GitHub Actions for automated releases:

  1. Update the version in pyproject.toml
  2. Create a new tag with git tag vX.Y.Z (e.g., git tag v0.1.0)
  3. Push the tag with git push --tags

This will automatically:

  • Verify the version in pyproject.toml matches the tag
  • Run tests and lint checks
  • Build and publish to PyPI
  • Build and publish to Docker Hub as oborchers/mcp-server-pacman:latest and oborchers/mcp-server-pacman:X.Y.Z

Project Structure

The codebase is organized into the following structure:

src/mcp_server_pacman/
├── models/             # Data models/schemas
├── providers/          # Package registry API clients
│   ├── pypi.py         # PyPI API functions
│   ├── npm.py          # npm API functions
│   ├── crates.py       # crates.io API functions
│   ├── dockerhub.py    # Docker Hub API functions
│   └── terraform.py    # Terraform Registry API functions
├── utils/              # Utilities and helpers
│   ├── cache.py        # Caching functionality
│   ├── constants.py    # Shared constants
│   └── parsers.py      # HTML parsing utilities
├── __init__.py         # Package initialization
├── __main__.py         # Entry point
└── server.py           # MCP server implementation

Tests follow a similar structure:

tests/
├── integration/        # Integration tests (real API calls)
├── models/             # Model validation tests
├── providers/          # Provider function tests
└── utils/              # Test utilities

FAQ

Common questions

Discussion

Questions & comments · 0

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