Package Manager▌
by oborchers
Easily search npm and other repositories with Package Manager. Get package, version, and dependency info fast. Supports
Integrates with package repositories including PyPI, npm, crates.io, Docker Hub, and Terraform Registry to search and retrieve detailed information about packages, versions, dependencies, and Docker images.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Developers researching package dependencies
- / DevOps engineers managing container images
- / Infrastructure teams working with Terraform modules
capabilities
- / Search packages across PyPI, npm, crates.io, and Terraform Registry
- / Get detailed package information including versions and dependencies
- / Search Docker images on Docker Hub
- / Retrieve Docker image metadata and tags
- / Check latest versions of Terraform modules
what it does
Search and retrieve detailed information about packages across multiple repositories including PyPI, npm, crates.io, Docker Hub, and Terraform Registry.
about
Package Manager is a community-built MCP server published by oborchers that provides AI assistants with tools and capabilities via the Model Context Protocol. Easily search npm and other repositories with Package Manager. Get package, version, and dependency info fast. Supports It is categorized under developer tools. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.
how to install
You can install Package Manager in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Package Manager is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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.
<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 indicesindex(string, required): Package index to search ("pypi", "npm", "crates", "terraform")query(string, required): Package name or search querylimit(integer, optional): Maximum number of results to return (default: 5, max: 50)
-
package_info- Get detailed information about a specific packageindex(string, required): Package index to query ("pypi", "npm", "crates", "terraform")name(string, required): Package nameversion(string, optional): Specific version to get info for (default: latest)
-
search_docker_image- Search for Docker images in Docker Hubquery(string, required): Image name or search querylimit(integer, optional): Maximum number of results to return (default: 5, max: 50)
-
docker_image_info- Get detailed information about a specific Docker imagename(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 modulename(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 nameversion(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 nameversion(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 nameversion(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.
<details> <summary>Using uvx</summary>Note that the
mcpkey is needed when using themcp.jsonfile.
{
"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:
- Update the version in
pyproject.toml - Create a new tag with
git tag vX.Y.Z(e.g.,git tag v0.1.0) - Push the tag with
git push --tags
This will automatically:
- Verify the version in
pyproject.tomlmatches the tag - Run tests and lint checks
- Build and publish to PyPI
- Build and publish to Docker Hub as
oborchers/mcp-server-pacman:latestandoborchers/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.
FAQ
- What is the Package Manager MCP server?
- Package Manager is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Package Manager?
- This profile displays 56 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
List & Promote Your MCP Server
Share your MCP server with the developer community
Ratings
4.6★★★★★56 reviews- ★★★★★James Reddy· Dec 28, 2024
Package Manager is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Sakura Martin· Dec 16, 2024
Package Manager is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Mei Flores· Dec 12, 2024
Package Manager reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Harper Johnson· Dec 12, 2024
Strong directory entry: Package Manager surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Dhruvi Jain· Dec 8, 2024
Package Manager is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Oshnikdeep· Nov 27, 2024
Package Manager is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Charlotte Abbas· Nov 19, 2024
Package Manager is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Mei Zhang· Nov 7, 2024
Package Manager is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Sakura Harris· Nov 3, 2024
Useful MCP listing: Package Manager is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Mei Chen· Nov 3, 2024
We wired Package Manager into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
showing 1-10 of 56