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mcp-server-pacman

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Python
Remote

Logotipo de Pacman

Servidor Pacman MCP

Un servidor de Protocolo de Contexto de Modelo que ofrece funciones de consulta de índices de paquetes. Este servidor permite a los LLM buscar y recuperar información de repositorios de paquetes como PyPI, npm, crates.io, Docker Hub y Terraform Registry.

Herramientas disponibles

  • search_package - Busca paquetes en los índices de paquetes

    • index (cadena, obligatorio): Índice del paquete a buscar ("pypi", "npm", "crates", "terraform")

    • query (cadena, obligatoria): nombre del paquete o consulta de búsqueda

    • limit (entero, opcional): número máximo de resultados a devolver (predeterminado: 5, máximo: 50)

  • package_info - Obtener información detallada sobre un paquete específico

    • index (cadena, obligatorio): índice del paquete a consultar ("pypi", "npm", "crates", "terraform")

    • name (cadena, obligatorio): nombre del paquete

    • version (cadena, opcional): versión específica para la que obtener información (predeterminado: la más reciente)

  • search_docker_image : busca imágenes de Docker en Docker Hub

    • query (cadena, obligatoria): Nombre de la imagen o consulta de búsqueda

    • limit (entero, opcional): número máximo de resultados a devolver (predeterminado: 5, máximo: 50)

  • docker_image_info : obtenga información detallada sobre una imagen de Docker específica

    • name (cadena, obligatorio): nombre de la imagen (por ejemplo, usuario/repositorio o biblioteca/repositorio)

    • tag (cadena, opcional): etiqueta de imagen específica (predeterminada: última)

  • terraform_module_latest_version : obtener la última versión de un módulo de Terraform

    • name (cadena, obligatorio): Nombre del módulo (formato: espacio de nombres/nombre/proveedor)

Indicaciones

  • búsqueda_pypi

    • Buscar paquetes de Python en PyPI

    • Argumentos:

      • query (cadena, obligatoria): nombre del paquete o consulta de búsqueda

  • información de pypi

    • Obtener información sobre un paquete específico de Python

    • Argumentos:

      • name (cadena, obligatorio): nombre del paquete

      • version (cadena, opcional): versión específica

  • búsqueda_npm

    • Buscar paquetes de JavaScript en npm

    • Argumentos:

      • query (cadena, obligatoria): nombre del paquete o consulta de búsqueda

  • información npm

    • Obtener información sobre un paquete de JavaScript específico

    • Argumentos:

      • name (cadena, obligatorio): nombre del paquete

      • version (cadena, opcional): versión específica

  • cajas de búsqueda

    • Busque paquetes de Rust en crates.io

    • Argumentos:

      • query (cadena, obligatoria): nombre del paquete o consulta de búsqueda

  • información de cajas

    • Obtenga información sobre un paquete específico de Rust

    • Argumentos:

      • name (cadena, obligatorio): nombre del paquete

      • version (cadena, opcional): versión específica

  • búsqueda_docker

    • Buscar imágenes de Docker en Docker Hub

    • Argumentos:

      • query (cadena, obligatoria): Nombre de la imagen o consulta de búsqueda

  • información del contenedor

    • Obtener información sobre una imagen de Docker específica

    • Argumentos:

      • name (cadena, obligatorio): nombre de la imagen (por ejemplo, usuario/repositorio)

      • tag (cadena, opcional): etiqueta específica

  • búsqueda_terraform

    • Busque módulos de Terraform en el Registro de Terraform

    • Argumentos:

      • query (cadena, obligatoria): nombre del módulo o consulta de búsqueda

  • información de terraform

    • Obtenga información sobre un módulo específico de Terraform

    • Argumentos:

      • name (cadena, obligatorio): Nombre del módulo (formato: espacio de nombres/nombre/proveedor)

  • terraform_última_versión

    • Obtenga la última versión de un módulo específico de Terraform

    • Argumentos:

      • name (cadena, obligatorio): Nombre del módulo (formato: espacio de nombres/nombre/proveedor)

Instalación

Uso de uv (recomendado)

Al usar uv , no se requiere ninguna instalación específica. Usaremos uvx para ejecutar directamente mcp-server-pacman .

Uso de PIP

Alternativamente, puede instalar mcp-server-pacman a través de pip:

pip install mcp-server-pacman

Después de la instalación, puedes ejecutarlo como un script usando:

python -m mcp_server_pacman

Usando Docker

También puedes utilizar la imagen de Docker:

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

Related MCP server: JSR MCP

Configuración

Configurar para Claude.app

Añade a tu configuración de Claude:

"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"]
  }
}

Configurar para VS Code

Para la instalación manual, agregue el siguiente bloque JSON a su archivo de configuración de usuario (JSON) en VS Code. Para ello, presione Ctrl + Shift + P y escriba Preferences: Open User Settings (JSON) .

Opcionalmente, puede agregarlo a un archivo llamado .vscode/mcp.json en su espacio de trabajo. Esto le permitirá compartir la configuración con otros.

Tenga en cuenta que la clave mcp es necesaria cuando se utiliza el archivo mcp.json .

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

Personalización - Agente de usuario

De forma predeterminada, el servidor utilizará el agente de usuario:

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

Esto se puede personalizar agregando el argumento --user-agent=YourUserAgent a la lista de args en la configuración.

Desarrollo

Ejecución de pruebas

  • Ejecutar todas las pruebas:

    uv run pytest -xvs

  • Ejecutar categorías de pruebas específicas:

    # 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

  • Comprobar el estilo del código:

    uv run ruff check .
uv run ruff format --check .

  • Código de formato:

    uv run ruff format .

Depuración

Puede usar el inspector MCP para depurar el servidor. Para instalaciones uvx:

npx @modelcontextprotocol/inspector uvx mcp-server-pacman

O si ha instalado el paquete en un directorio específico o está desarrollando en él:

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

Proceso de liberación

El proyecto utiliza GitHub Actions para lanzamientos automatizados:

  1. Actualice la versión en pyproject.toml

  2. Crea una nueva etiqueta con git tag vX.YZ (por ejemplo, git tag v0.1.0 )

  3. Empuje la etiqueta con git push --tags

Esto automáticamente:

  • Verifique que la versión en pyproject.toml coincida con la etiqueta

  • Ejecutar pruebas y comprobaciones de pelusa

  • Construir y publicar en PyPI

  • Construya y publique en Docker Hub como oborchers/mcp-server-pacman:latest y oborchers/mcp-server-pacman:XYZ

Estructura del proyecto

La base del código está organizada en la siguiente estructura:

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

Las pruebas siguen una estructura similar:

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

Contribuyendo

Invitamos a todos a contribuir para expandir y mejorar mcp-server-pacman. Ya sea que desee agregar nuevos índices de paquetes, mejorar la funcionalidad existente o mejorar la documentación, su aporte es valioso.

Para ver ejemplos de otros servidores MCP y patrones de implementación, consulte: https://github.com/modelcontextprotocol/servers

¡Aceptamos solicitudes de incorporación de cambios! Siéntete libre de contribuir con nuevas ideas, correcciones de errores o mejoras para que mcp-server-pacman sea aún más potente y útil.

Licencia

mcp-server-pacman está licenciado bajo la Licencia MIT. Esto significa que usted tiene libertad de usar, modificar y distribuir el software, sujeto a los términos y condiciones de la Licencia MIT. Para más detalles, consulte el archivo de LICENCIA en el repositorio del proyecto.

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.

  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
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 5 tools

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

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