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PedroDnT

MCP Deep Web Research Server

by PedroDnT
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Servidor de investigación web profunda MCP (v0.3.0)

Versión de Node.jsMecanografiado Licencia: MIT insignia de herrería

Un servidor de Protocolo de Contexto Modelo (MCP) para investigación web avanzada.

Últimos cambios

  • Se agregó la herramienta visit_page para la extracción directa de contenido de la página web

  • Rendimiento optimizado para trabajar dentro de los límites de tiempo de espera de MCP

    • Parámetros maxDepth y maxBranching predeterminados reducidos

    • Eficiencia de carga de páginas mejorada

    • Se agregaron controles de tiempo de espera durante todo el proceso.

    • Manejo mejorado de errores en tiempos de espera

Este proyecto es una bifurcación de mcp-webresearch de mzxrai , mejorada con funciones adicionales para la investigación en la web profunda. Agradecemos a los creadores originales su labor fundacional.

Incorpore información en tiempo real a Claude con colas de búsqueda inteligentes, extracción de contenido mejorada y capacidades de investigación profunda.

Related MCP server: MCP Web Research Server

Características

  • Sistema de cola de búsqueda inteligente

    • Operaciones de búsqueda por lotes con limitación de velocidad

    • Gestión de colas con seguimiento del progreso

    • Recuperación de errores y reintentos automáticos

    • Desduplicación de resultados de búsqueda

  • Extracción de contenido mejorada

    • Puntuación de relevancia basada en TF-IDF

    • Análisis de proximidad de palabras clave

    • Ponderación de la sección de contenido

    • Puntuación de legibilidad

    • Análisis mejorado de la estructura HTML

    • Extracción de datos estructurados

    • Mejor limpieza y formato de contenido

  • Características principales

    • Integración de búsqueda de Google

    • Extracción de contenido de páginas web

    • Seguimiento de sesiones de investigación

    • Conversión de Markdown con formato mejorado

Prerrequisitos

Instalación

Instalación mediante herrería

Para instalar Deep Web Research Server para Claude Desktop automáticamente a través de Smithery :

npx -y @smithery/cli install @PedroDnT/mcp-deepwebresearch --client claude

Instalación global (recomendada)

# Install globally using npm
npm install -g mcp-deepwebresearch

# Or using yarn
yarn global add mcp-deepwebresearch

# Or using pnpm
pnpm add -g mcp-deepwebresearch

Instalación de proyecto local

# Using npm
npm install mcp-deepwebresearch

# Using yarn
yarn add mcp-deepwebresearch

# Using pnpm
pnpm add mcp-deepwebresearch

Integración de escritorio de Claude

Después de instalar el paquete, agregue esta entrada a su claude_desktop_config.json :

Ventanas

{
  "mcpServers": {
    "deepwebresearch": {
      "command": "mcp-deepwebresearch",
      "args": []
    }
  }
}

Ubicación: %APPDATA%\Claude\claude_desktop_config.json

macOS

{
  "mcpServers": {
    "deepwebresearch": {
      "command": "mcp-deepwebresearch",
      "args": []
    }
  }
}

Ubicación: ~/Library/Application Support/Claude/claude_desktop_config.json

Esta configuración permite que Claude Desktop inicie automáticamente el servidor de investigación web MCP cuando sea necesario.

Configuración por primera vez

Después de la instalación, ejecute este comando para instalar las dependencias necesarias del navegador:

npx playwright install chromium

Uso

Simplemente inicia un chat con Claude y envía una propuesta que se beneficie de una investigación web. Si deseas una propuesta prediseñada y personalizada para una investigación web más profunda, puedes usar la propuesta agentic-research que ofrecemos en este paquete. Accede a esa propuesta en Claude Desktop haciendo clic en el icono del clip en la entrada del chat y seleccionando " Choose an integration → deepwebresearch → agentic-research .

Herramientas

  1. deep_research

    • Realiza una investigación exhaustiva con análisis de contenido.

    • Argumentos:

      {
  topic: string;
  maxDepth?: number;      // default: 2
  maxBranching?: number;  // default: 3
  timeout?: number;       // default: 55000 (55 seconds)
  minRelevanceScore?: number;  // default: 0.7
}

    • Devoluciones:

      {
  findings: {
    mainTopics: Array<{name: string, importance: number}>;
    keyInsights: Array<{text: string, confidence: number}>;
    sources: Array<{url: string, credibilityScore: number}>;
  };
  progress: {
    completedSteps: number;
    totalSteps: number;
    processedUrls: number;
  };
  timing: {
    started: string;
    completed?: string;
    duration?: number;
    operations?: {
      parallelSearch?: number;
      deduplication?: number;
      topResultsProcessing?: number;
      remainingResultsProcessing?: number;
      total?: number;
    };
  };
}

  2. parallel_search

    • Realiza múltiples búsquedas de Google en paralelo con cola inteligente

    • Argumentos: { queries: string[], maxParallel?: number }

    • Nota: maxParallel está limitado a 5 para garantizar un rendimiento confiable

  3. visit_page

    • Visita una página web y extrae su contenido

    • Argumentos: { url: string }

    • Devoluciones:

      {
  url: string;
  title: string;
  content: string;  // Markdown formatted content
}

Indicaciones

agentic-research

Una guía de investigación que ayuda a Claude a realizar una investigación web exhaustiva. La guía le indica a Claude que:

  • Comience con búsquedas amplias para comprender el panorama temático.

  • Priorizar fuentes confiables y de alta calidad

  • Refinar iterativamente la dirección de la investigación en función de los hallazgos

  • Manténgase informado y permítanos guiar la investigación de forma interactiva.

  • Cite siempre las fuentes con URL

Opciones de configuración

El servidor se puede configurar a través de variables de entorno:

  • MAX_PARALLEL_SEARCHES : Número máximo de búsquedas simultáneas (predeterminado: 5)

  • SEARCH_DELAY_MS : Retraso entre búsquedas en milisegundos (valor predeterminado: 200)

  • MAX_RETRIES : Número de reintentos para solicitudes fallidas (valor predeterminado: 3)

  • TIMEOUT_MS : Tiempo de espera de la solicitud en milisegundos (valor predeterminado: 55000)

  • LOG_LEVEL : Nivel de registro (predeterminado: 'info')

Manejo de errores

Problemas comunes

  1. Limitación de velocidad

    • Síntoma: Error "Demasiadas solicitudes"

    • Solución: Aumente SEARCH_DELAY_MS o disminuya MAX_PARALLEL_SEARCHES

  2. Tiempos de espera de la red

    • Síntoma: Error "Tiempo de espera agotado"

    • Solución: Asegúrese de que las solicitudes se completen dentro del tiempo de espera de MCP de 60 segundos

  3. Problemas con el navegador

    • Síntoma: Error "No se pudo iniciar el navegador"

    • Solución: asegúrese de que Playwright esté instalado correctamente ( npx playwright install )

Depuración

Este software es beta. Si tiene algún problema:

  1. Consulte los registros MCP de Claude Desktop:

    # On macOS
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

# On Windows
Get-Content -Path "$env:APPDATA\Claude\logs\mcp*.log" -Tail 20 -Wait

  2. Habilitar el registro de depuración:

    export LOG_LEVEL=debug

Desarrollo

Configuración

# Install dependencies
pnpm install

# Build the project
pnpm build

# Watch for changes
pnpm watch

# Run in development mode
pnpm dev

Pruebas

# Run all tests
pnpm test

# Run tests in watch mode
pnpm test:watch

# Run tests with coverage
pnpm test:coverage

Calidad del código

# Run linter
pnpm lint

# Fix linting issues
pnpm lint:fix

# Type check
pnpm type-check

Contribuyendo

  1. Bifurcar el repositorio

  2. Crea tu rama de funciones ( git checkout -b feature/amazing-feature )

  3. Confirme sus cambios ( git commit -m 'Add some amazing feature' )

  4. Empujar a la rama ( git push origin feature/amazing-feature )

  5. Abrir una solicitud de extracción

Estándares de codificación

  • Siga las mejores prácticas de TypeScript

  • Mantener la cobertura de pruebas por encima del 80%

  • Documentar nuevas funciones y API

  • Actualice CHANGELOG.md para cambios significativos

  • Seguir el versionado semántico

Consideraciones de rendimiento

  • Utilice operaciones por lotes siempre que sea posible

  • Implementar un manejo adecuado de errores y reintentos

  • Considere el uso de memoria con grandes conjuntos de datos

  • Almacenar en caché los resultados cuando sea apropiado

  • Utilice la transmisión para contenido de gran tamaño

Requisitos

  • Node.js >= 18

  • Dramaturgo (instalado automáticamente como dependencia)

Plataformas verificadas

  • [x] macOS

  • [x] Ventanas

  • [ ] Linux

Licencia

Instituto Tecnológico de Massachusetts (MIT)

Créditos

Este proyecto se basa en el excelente trabajo de mcp-webresearch de mzxrai . El código base original sentó las bases para nuestras funciones y capacidades mejoradas.

Autor

qpd-v

Available Tools

3 tools
deep_researchC

Perform deep research on a topic with content extraction and analysis

ParametersJSON Schema
NameRequiredDescriptionDefault
topicYesResearch topic or question
maxDepthNoMaximum depth of related content exploration
maxBranchingNoMaximum number of related paths to explore
timeoutNoResearch timeout in milliseconds
minRelevanceScoreNoMinimum relevance score for including content

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'content extraction and analysis' but fails to detail critical aspects such as execution time, resource usage, error handling, or output format. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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 that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and avoids redundancy, making it highly concise and well-structured for quick comprehension.

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 complexity of a 'deep research' tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'deep research' entails, how results are returned, or any behavioral constraints, leaving the agent with inadequate information for effective use in a broader 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 description coverage is 100%, meaning all parameters are documented in the schema. The description adds no additional semantic context about parameters beyond implying 'deep research' involves branching and depth. This meets the baseline for high schema coverage but doesn't enhance understanding of parameter roles or interactions.

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 as 'Perform deep research on a topic with content extraction and analysis,' which specifies the verb (perform deep research) and resource (topic) with additional capabilities (content extraction and analysis). However, it doesn't explicitly differentiate from sibling tools like 'parallel_search' or 'visit_page,' 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 like 'parallel_search' or 'visit_page.' It lacks any context about appropriate scenarios, prerequisites, or exclusions, leaving the agent with minimal direction for tool selection.

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

visit_pageC

Visit a webpage and extract its content

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL to visit

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 'visit a webpage and extract its content', which implies a read operation, but doesn't specify details like authentication needs, rate limits, error handling, or what 'extract content' entails (e.g., HTML, text, metadata). For a tool with no annotations, this leaves significant gaps in understanding its 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 that directly states the tool's function without unnecessary words. It is front-loaded with the core action ('visit a webpage') and purpose ('extract its content'), making it easy to understand quickly. Every part of the sentence earns its place by conveying essential information.

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 web interaction tool with potential behavioral nuances) and the lack of annotations and output schema, the description is incomplete. It doesn't cover what 'extract content' means in terms of output format, error cases, or limitations. For a tool that interacts with external webpages, more context is needed to ensure proper usage.

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 'url' parameter clearly documented as 'URL to visit'. The description adds no additional meaning beyond this, as it doesn't elaborate on URL format constraints or extraction specifics. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to given the schema's clarity.

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 with a specific verb ('visit') and resource ('webpage'), and specifies the action ('extract its content'). However, it doesn't differentiate this tool from potential sibling tools like 'deep_research' or 'parallel_search', which might have overlapping functionality. The description is not tautological but lacks sibling distinction.

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. It doesn't mention any context, prerequisites, or exclusions, and doesn't reference sibling tools like 'deep_research' or 'parallel_search' that might be related. Usage is implied only by the tool's name and description, with no explicit guidelines.

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. 3 tool updates
    • First observeddeep_research
    • First observedparallel_search
    • First observedvisit_page
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

B3.1/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: deep_research focuses on comprehensive topic analysis, parallel_search handles multiple Google searches, and visit_page extracts content from specific webpages. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency3/5

The naming is mixed: deep_research and parallel_search use snake_case with descriptive names, while visit_page also uses snake_case but is more action-oriented. There is no consistent verb_noun pattern, but the names are still readable and understandable.

Tool Count3/5

With only 3 tools, the server feels thin for a 'Deep Web Research' scope, which might imply more comprehensive capabilities like data analysis or report generation. However, the tools cover core search and extraction tasks, so it's borderline but not severely lacking.

Completeness3/5

The tools cover basic web research tasks (searching, visiting, deep analysis), but there are notable gaps such as no tools for saving results, managing research sessions, or advanced data processing. Agents can work around this, but the surface is not fully comprehensive for deep web research.

Maintenance

ActivityInactive
ResponsivenessNo issues

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