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DocuMind MCP Server

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Servidor DocuMind MCP

"Donde la documentación se fusiona con la inteligencia digital"

Un servidor de Protocolo de Contexto de Modelo (MCP) de próxima generación que revoluciona el análisis de la calidad de la documentación a través del procesamiento neuronal avanzado.

⚡ Sistemas centrales

  • Análisis de documentación neuronal : algoritmos avanzados para una evaluación completa de README

  • Escaneo de encabezado holográfico : análisis SVG de vanguardia para elementos visuales

  • 🌍 Compatibilidad lingüística multidimensional : verificación de documentación interlingüística

  • 💫 Motor de sugerencias cuánticas : recomendaciones de mejora impulsadas por IA

Related MCP server: Lucidity MCP

🚀 Secuencia de arranque del sistema

Requisitos del sistema

  • Node.js 18+

  • npm || hilo

Inicializar núcleo

npm install

Compilar matriz

npm run build

Enlace del desarrollo neuronal

Establecer conexión neuronal en tiempo real:

npm run watch

Protocolo de Operación

Configuración del sistema

Integración con el mainframe de Claude Desktop:

Terminal de Windows :

// %APPDATA%/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "documind-mcp-server": {
      "command": "/path/to/documind-mcp-server/build/index.js"
    }
  }
}

Comandos de interfaz neuronal

evaluar_readme

Inicia el análisis cuántico de la estructura de la documentación.

Parámetros:

  • projectPath : Ruta neuronal al directorio de destino

Ejemplo de solicitud:

{
  name: "evaluate_readme",
  arguments: {
    projectPath: "/path/to/project"
  }
}

Ejemplo de respuesta:

{
  content: [
    {
      type: "text",
      text: JSON.stringify({
        filePath: "/path/to/project/README.md",
        hasHeaderImage: true,
        headerImageQuality: {
          hasGradient: true,
          hasAnimation: true,
          // ... other quality metrics
        },
        score: 95,
        suggestions: [
          "Consider adding language badges",
          // ... other suggestions
        ]
      })
    }
  ]
}

Matriz de desarrollo

Protocolo de depuración

Acceda a la red neuronal a través de MCP Inspector:

npm run inspector

Guía de solución de problemas

Problemas comunes y soluciones

  1. Imagen de encabezado no detectada

    • Asegúrese de que el archivo SVG esté ubicado en el directorio assets/

    • Validar que el archivo SVG contenga la estructura XML adecuada

    • Comprobar permisos de archivos

  2. Insignias de idioma no reconocidas

    • Verificar que las insignias usen el formato shields.io

    • Compruebe que la estructura HTML siga el patrón recomendado

    • Asegúrese de que la alineación central sea la adecuada

  3. Errores de compilación

    • Borrar node_modules y reinstalar las dependencias

    • Asegúrese de que la versión de TypeScript coincida con los requisitos del proyecto

    • Comprobar errores de sintaxis en archivos modificados

  4. Problemas de conexión de MCP

    • Verificar la configuración del transporte de stdio

    • Comprobar la configuración de Claude Desktop

    • Asegúrese de que las rutas de archivo sean correctas en la configuración

Optimización del rendimiento

  1. Análisis SVG

    • Minimizar la complejidad de SVG para un análisis más rápido

    • Utilice degradados y animaciones eficientes

    • Optimice el tamaño del archivo manteniendo la calidad

  2. Escaneo de README

    • Estructurar el contenido para un análisis óptimo

    • Utilice patrones de rebajas recomendados

    • Siga las pautas de colocación de insignias

🔬 Documentación de la API

Clases principales

Servicio Readme

Servicio principal para el análisis y evaluación de README.

class ReadmeService {
  // Analyzes all README files in a project
  async evaluateAllReadmes(projectPath: string): Promise<ReadmeEvaluation[]>
  
  // Evaluates a single README file
  private async evaluateReadme(dirPath: string, readmePath: string): Promise<ReadmeEvaluation>
  
  // Evaluates language badge configuration
  private evaluateLanguageBadges(content: string): BadgeEvaluation
}

Servicio SVG

Servicio especializado para el análisis de imágenes de encabezado SVG.

class SVGService {
  // Evaluates SVG header image quality
  public evaluateHeaderImageQuality(imgSrc: string, content: string): HeaderImageQuality
  
  // Checks for project-specific elements in SVG
  private checkProjectSpecificImage(svgContent: string, readmeContent: string): boolean
}

Interfaces centrales

interface ReadmeEvaluation {
  filePath: string;
  hasHeaderImage: boolean;
  headerImageQuality: HeaderImageQuality;
  isCentered: {
    headerImage: boolean;
    title: boolean;
    badges: boolean;
  };
  hasBadges: {
    english: boolean;
    japanese: boolean;
    isCentered: boolean;
    hasCorrectFormat: boolean;
  };
  score: number;
  suggestions: string[];
}

interface HeaderImageQuality {
  hasGradient: boolean;
  hasAnimation: boolean;
  hasRoundedCorners: boolean;
  hasEnglishText: boolean;
  isProjectSpecific: boolean;
}

Manejo de errores

El servidor implementa un manejo integral de errores:

try {
  const evaluations = await readmeService.evaluateAllReadmes(projectPath);
  // Process results
} catch (error) {
  const errorMessage = error instanceof Error ? error.message : String(error);
  return {
    content: [{
      type: 'text',
      text: `Evaluation error: ${errorMessage}`
    }],
    isError: true
  };
}

⚡ Licencia

Operando bajo el protocolo MIT.

Available Tools

1 tool
evaluate_readmeC

リポジトリ内の全てのREADMEファイルの構成を評価し、改善点を提案します

ParametersJSON Schema
NameRequiredDescriptionDefault
projectPathYesプロジェクトのルートディレクトリパス

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 mentions evaluation and proposal of improvements but does not specify how the evaluation is performed (e.g., criteria, depth), what format the proposals take, whether it modifies files or only reports, or any constraints like rate limits or permissions needed. 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, efficient sentence in Japanese that directly states the tool's function without unnecessary words. It is front-loaded with the core action and outcome, making it easy to parse. This minimal structure earns a top score for conciseness.

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 (evaluating and proposing improvements for README files) and the absence of annotations and output schema, the description is insufficient. It lacks details on evaluation criteria, output format, behavioral traits, and usage context. Without this information, an AI agent would struggle to understand the full scope and limitations of the tool.

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 single parameter 'projectPath' clearly documented as 'プロジェクトのルートディレクトリパス' (project root directory path). The description does not add any additional meaning or context beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately handles parameter semantics.

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: 'evaluate the structure of all README files in a repository and propose improvements.' It specifies the verb ('evaluate'), resource ('README files'), and outcome ('propose improvements'), making the intent unambiguous. However, since there are no sibling tools, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.

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 exclusions. It simply states what the tool does without context for its application. This lack of usage instructions limits its effectiveness for an AI agent in selecting the right tool for a scenario.

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 updatev1.0.0
    • First observedevaluate_readme
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool has a single, clear purpose of evaluating README files in repositories.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'evaluate_readme' follows a clear verb_noun pattern.

Tool Count2/5

One tool is too few for a server named 'DocuMind MCP Server', which suggests a broader documentation-related purpose. This feels thin and limited in scope, lacking coverage for other documentation tasks.

Completeness1/5

The server is severely incomplete for its implied domain of documentation management. It only handles README evaluation, missing obvious gaps like creating, updating, or analyzing other documentation types, or managing documentation workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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