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servidor fabric-mcp

Tabla de contenido

  1. Introducción

  2. ¿Qué es el Protocolo de Contexto Modelo (MCP)?

  3. Características

  4. Herramientas

  5. Instalación

  6. Uso

  7. Configuración para uso con VS Code

  8. Consejos para usarlo con Cline

  9. Solución de problemas

  10. Contribuyendo

  11. Licencia

Related MCP server: wrapmcp

Introducción

fabric-mcp-server es un servidor de Protocolo de Contexto de Modelo (MCP) diseñado para exponer patrones de Fabric como herramientas de integración con Cline. Esta integración mejora las capacidades de Cline al aprovechar la ejecución de patrones basada en IA desde el repositorio de Fabric.

¿Qué es el Protocolo de Contexto Modelo (MCP)?

El Protocolo de Contexto de Modelo (MCP) es una especificación que facilita la comunicación entre los sistemas de IA y herramientas o recursos externos. Estandariza la forma en que los modelos de IA interactúan con diversas capacidades, como bases de datos, API y sistemas de archivos. Los servidores MCP, como fabric-mcp-server , implementan este protocolo para que las herramientas y los recursos sean accesibles a los modelos de IA, ampliando así su alcance funcional.

Características

  • Expone patrones de tela como herramientas : el servidor hace que todos los patrones de tela estén disponibles como herramientas individuales dentro de Cline.

  • Ejecución de patrones : los usuarios pueden seleccionar y ejecutar patrones de Fabric directamente dentro de las tareas de Cline.

  • Capacidades mejoradas : integra la ejecución de patrones impulsada por IA para aumentar la funcionalidad de Cline.

Herramientas

fabric-mcp-server expone una amplia gama de patrones de Fabric como herramientas. Algunos ejemplos incluyen:

  • analyze_claims

  • summarize

  • extract_wisdom

  • create_mermaid_visualization

  • Y muchos más...

Para ver la lista completa de patrones disponibles, puede enumerar los directorios en el directorio fabric/patterns .

Instalación

  1. Clonar el repositorio : clona el repositorio fabric-mcp-server en tu sistema local.

  2. Instalar dependencias : navegue al directorio fabric-mcp-server y ejecute npm install .

  3. Construya el proyecto : ejecute npm run build para compilar el código TypeScript.

Uso

Para utilizar fabric-mcp-server con Cline:

  1. Asegúrese de que el servidor esté instalado y en funcionamiento.

  2. Configure el servidor MCP en su archivo de configuración de Cline.

  3. Cree una nueva tarea en Cline y seleccione un patrón de tela para usar.

Configuración para uso con VS Code

  1. Clonar el repositorio : clona el repositorio fabric-mcp-server en tu sistema local.

  2. Instalar dependencias : navegue al directorio fabric-mcp-server y ejecute npm install .

  3. Construya el proyecto : ejecute npm run build .

  4. Configurar los ajustes de Cline : Agregue la configuración del servidor MCP a su archivo de configuración de Cline. La ruta del archivo varía según el sistema operativo:

    • Windows : C:\Users\<username>\AppData\Roaming\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json

    • macOS : ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

    • Linux : ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

    Utilice la siguiente configuración:

"fabric-mcp-server": {
  "command": "node",
  "args": [
    "<path-to-fabric-mcp-server>/build/index.js"
  ],
  "env": {},
  "disabled": false,
  "autoApprove": [],
  "transportType": "stdio",
  "timeout": 60
}

Reemplace <path-to-fabric-mcp-server> con la ruta real al directorio fabric-mcp-server en su sistema. Por ejemplo:

  • Windows : "C:\\path\\to\\fabric-mcp-server\\build\\index.js"

  • macOS/Linux : "/path/to/fabric-mcp-server/build/index.js"

  1. Reiniciar VSCode : reinicie VSCode o vuelva a cargar la extensión Cline para aplicar los cambios.

Consejos para usarlo con Cline

Para maximizar los beneficios de fabric-mcp-server con Cline, agregue use fabric-mcp-server al final de sus indicaciones o considere agregar la siguiente regla a su archivo .clinerules :

# Fabric MCP Server Rule
1. **List Fabric Patterns**: When a new task is created, list all pattern names from the Fabric repository.
2. **Prompt for Pattern Selection**: Ask the user to select one of the following options:
   a) Enter a pattern name from the list to use the `fabric-mcp-server` tool with the specified pattern.
   b) Choose not to use `fabric-mcp-server` for the task.

Esta regla agiliza el proceso de selección de herramientas para nuevas tareas en Cline.

Solución de problemas

  • Asegúrese de que fabric-mcp-server esté configurado correctamente en la configuración de Cline.

  • Verifique que el servidor esté ejecutándose y sea accesible.

  • Verifique la salida de la consola para ver si hay mensajes de error.

Contribuyendo

Se agradecen las contribuciones a fabric-mcp-server . Consulte el archivo CONTRIBUTING.md para obtener instrucciones sobre cómo contribuir.

Licencia

fabric-mcp-server se publica bajo la licencia MIT .

Available Tools

1 tool
recommend_toolC

Recommends the best Fabric pattern tool for a given task

ParametersJSON Schema
NameRequiredDescriptionDefault
inputYesThe user's task description

TDQS

C2.9/5.0
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 of behavioral disclosure. It states the tool 'recommends' but does not clarify how recommendations are generated (e.g., based on criteria, algorithms, or data sources), whether it requires specific permissions, or what the output format entails. This leaves significant gaps in understanding the tool's 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 description is a single, clear sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently conveys the essential information, making it highly concise and well-structured.

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?

Given the tool's complexity (a recommendation function with no annotations or output schema), the description is incomplete. It lacks details on how recommendations are made, what criteria are used, the format of the output, or any behavioral traits. This makes it inadequate for an agent to fully understand and use the tool effectively.

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, with the parameter 'input' documented as 'The user's task description.' The description adds no additional meaning beyond this, such as examples or constraints. According to the rules, when schema coverage is high (>80%), the baseline score is 3, which applies here.

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 tool's purpose: 'Recommends the best Fabric pattern tool for a given task.' It specifies the verb ('recommends') and resource ('Fabric pattern tool'), making the function understandable. However, with no sibling tools provided, it cannot demonstrate differentiation from alternatives, preventing a score of 5.

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?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or specific contexts. It merely restates the tool's function without indicating appropriate scenarios or exclusions, which is insufficient for effective agent decision-making.

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. 1 tool update
    • First observedrecommend_tool

TDQS

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to confuse it with. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

The single tool name 'recommend_tool' follows a consistent verb_noun pattern, and with only one tool, there is no inconsistency to evaluate. The naming is clear and predictable.

Tool Count2/5

A single tool is too few for most server purposes, as it severely limits functionality and scope. This feels thin and incomplete for a server named 'Fabric MCP Server', which might imply broader capabilities.

Completeness1/5

The server is severely incomplete; with only a recommendation tool, there are obvious gaps in the surface. It lacks any tools to actually execute or manage Fabric patterns, making it impossible for agents to perform core tasks beyond getting advice.

Maintenance

ActivityInactive
ResponsivenessNo issues

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