mcp-server-pacman

mcp-server-pacman

mcp-server-pacman

Category
访问服务器

Tools

search_package

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

package_info

Get detailed information about a specific package

search_docker_image

Search for Docker images in Docker Hub

docker_image_info

Get detailed information about a specific Docker image

terraform_module_latest_version

Get the latest version of a Terraform module

README

Pacman Logo

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.

<a href="https://glama.ai/mcp/servers/@oborchers/mcp-server-pacman"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@oborchers/mcp-server-pacman/badge" alt="mcp-server-pacman MCP server" /> </a>

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:

<details> <summary>Using uvx</summary>

"mcpServers": {
  "pacman": {
    "command": "uvx",
    "args": ["mcp-server-pacman"]
  }
}

</details>

<details> <summary>Using docker</summary>

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

</details>

<details> <summary>Using pip installation</summary>

"mcpServers": {
  "pacman": {
    "command": "python",
    "args": ["-m", "mcp-server-pacman"]
  }
}

</details>

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.

<details> <summary>Using uvx</summary>

{
  "mcp": {
    "servers": {
      "pacman": {
        "command": "uvx",
        "args": ["mcp-server-pacman"]
      }
    }
  }
}

</details>

<details> <summary>Using Docker</summary>

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

</details>

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

Contributing

We encourage contributions to help expand and improve mcp-server-pacman. Whether you want to add new package indices, enhance existing functionality, or improve documentation, your input is valuable.

For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers

Pull requests are welcome! Feel free to contribute new ideas, bug fixes, or enhancements to make mcp-server-pacman even more powerful and useful.

License

mcp-server-pacman is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选