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mcp-server-pacman

by oborchers
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Python
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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.

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

Related MCP server: JSR MCP

Configuration

Configure for Claude.app

Add to your Claude settings:

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

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

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.

Available Tools

5 tools
docker_image_infoC

Get detailed information about a specific Docker image

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYesImage name (e.g., user/repo or library/repo)
tagNoSpecific image tag (default: latest)

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It only states a generic 'get information' without specifying side effects, required permissions, network dependencies, or the nature of the returned data. This is insufficient for an agent to anticipate tool behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence is concise and front-loaded with the key action. No extraneous words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 2 parameters and no output schema. The description fails to explain what 'detailed information' includes (e.g., layers, config, metadata). An agent cannot predict the return format or completeness without additional context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (both parameters have descriptions). The tool description adds no additional meaning beyond what the schema provides. Per guidelines, baseline 3 applies; the description does not enhance understanding of parameter usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get detailed information about a specific Docker image' clearly states the tool's purpose with a specific verb ('Get') and resource ('Docker image'). However, it does not differentiate from sibling tools like search_docker_image, leaving ambiguity about what 'detailed information' entails.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as search_docker_image or package_info. The agent receives no indication of prerequisites, exclusions, or appropriate context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

package_infoC

Get detailed information about a specific package

ParametersJSON Schema
NameRequiredDescriptionDefault
indexYesPackage index to query (pypi, npm, crates, terraform)
nameYesPackage name
versionNoSpecific version to get info for (default: latest)

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description does not disclose any behavioral traits beyond the basic action. It does not confirm whether the operation is read-only, destructive, or has any side effects, which is a significant gap for a tool likely performing external queries.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence with no wasted words. It is efficiently front-loaded with the action and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, yet the description only vaguely says 'detailed information'. It does not specify what fields or structure the response contains, leaving the agent without adequate context for handling the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for parameters, so the description adds no additional meaning beyond what the schema already provides. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and resource 'detailed information about a specific package'. It is specific enough to distinguish from sibling tools like 'search_package' and 'docker_image_info', though it does not explicitly differentiate them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool vs alternatives. There is no mention of prerequisites, context, or exclusions, leaving the agent without decision support.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_docker_imageB

Search for Docker images in Docker Hub

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesImage name or search query
limitNoMaximum number of results to return

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, but it only states the function without disclosing behavioral traits like read-only nature, rate limits, or default pagination. Basic search behavior is implied but not explicitly guaranteed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no repetition or fluff. While brief, it front-loads the core purpose, earning points for conciseness, though slightly more context could fit without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and simple parameters, the description suffices for a basic search. However, it does not clarify return format (e.g., tags, repositories, pagination), leaving some ambiguity for an agent. Adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers 100% of parameters with clear descriptions (e.g., 'Image name or search query', 'Maximum number of results'). The description adds no extra meaning beyond the schema, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Search' and the resource 'Docker images' with location 'Docker Hub', making the purpose unmistakable. It effectively distinguishes from sibling tools like `docker_image_info` and `search_package`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as `docker_image_info` (for details) or `search_package` (for non-Docker packages). The description lacks any context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_packageB

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

ParametersJSON Schema
NameRequiredDescriptionDefault
indexYesPackage index to search (pypi, npm, crates, terraform)
queryYesPackage name or search query
limitNoMaximum number of results to return

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It only states the basic purpose without disclosing behavioral traits such as rate limits, authentication requirements, error handling, or the structure of the response. This is minimal transparency for a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that immediately conveys the verb and resource. It is front-loaded and contains no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lacks information about the output format or what the search results contain. Since there is no output schema, the description should have provided context on the return structure to help the agent interpret results. This gap reduces completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for all parameters. The tool description adds no extra meaning beyond what the schema already provides (e.g., listing indices that match the enum). Baseline 3 is appropriate given high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the action 'search for packages' and the resource 'package indices', with explicit examples (PyPI, npm, crates.io, Terraform Registry). It effectively distinguishes from sibling tools like 'package_info' which likely provides details on a specific package.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for searching packages but does not provide explicit guidance on when to use this tool versus alternatives like 'package_info' or 'search_docker_image'. No exclusions or context-driven triggers are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

terraform_module_latest_versionB

Get the latest version of a Terraform module

ParametersJSON Schema
NameRequiredDescriptionDefault
nameYesModule name (format: namespace/name/provider)

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It only states 'Get', implying read-only, but no disclosure of potential errors, caching, rate limits, or behavior when module not found.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence. It is concise and front-loaded, though slightly minimal for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity (one parameter, no output schema, no annotations), the description is minimally complete. However, it lacks details about return values or error states, which are needed for full understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (the single parameter is well-described). The description does not add extra semantics beyond the schema's parameter description, earning a baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: 'Get the latest version of a Terraform module'. It uses a specific verb and resource, and distinguishes from sibling tools (docker_image_info, package_info, etc.) that deal with different domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. There is no mention of prerequisites, scenarios, or explicit when-to-use context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 5 tool updatesv1.0.0
    • First observeddocker_image_info
    • First observedpackage_info
    • First observedsearch_docker_image
    • First observedsearch_package
    • First observedterraform_module_latest_version
Install Server
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TDQS

B3.2/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: Docker images have separate search and info tools, packages similarly, and Terraform modules have a dedicated version lookup. No overlap between resources.

Naming Consistency3/5

Names use snake_case but the ordering of resource and action varies: e.g., 'docker_image_info' (resource_action) vs 'search_docker_image' (action_resource). 'terraform_module_latest_version' uses a different pattern with an adjective. This inconsistency could cause confusion.

Tool Count4/5

With 5 tools, the server is focused and well-scoped for an informational package manager. It covers Docker, general packages, and Terraform modules without being too sparse.

Completeness3/5

The server provides search and info for Docker and packages, which is reasonable for an informational tool. However, only one Terraform module operation exists, and missing CRUD operations like install or delete are on the boundary of the domain.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

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