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Perplexity MCP Server

by laodev1
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TypeScript
Remote

Servidor MCP de Perplexity

Descripción general

El servidor MCP de Perplexity es una implementación en Node.js del Protocolo de Contexto de Modelo (MCP) de Anthropic que permite a Claude interactuar con los modelos de lenguaje de Perplexity. Este servidor proporciona un puente seguro entre Claude y las capacidades de Perplexity AI, lo que permite interacciones mejoradas con la IA mediante el uso de herramientas.

Related MCP server: Perplexity MCP Server

Herramientas disponibles

El servidor actualmente implementa dos herramientas principales:

1. perplejidad_chat

Herramienta avanzada para completar chats con soporte para historial completo de mensajes.

{
  "name": "perplexity_chat",
  "description": "Generate a chat completion using Perplexity AI",
  "parameters": {
    "model": "string (optional) - One of: llama-3.1-sonar-small-128k-online, llama-3.1-sonar-large-128k-online, llama-3.1-sonar-huge-128k-online",
    "messages": "array of {role, content} objects - The conversation history",
    "temperature": "number (optional) - Sampling temperature between 0-2"
  }
}

2. perplejidad_pregunta

Interfaz de consulta única simplificada para preguntas rápidas.

{
  "name": "perplexity_ask",
  "description": "Send a simple query to Perplexity AI",
  "parameters": {
    "query": "string - The question or prompt to send",
    "model": "string (optional) - One of: llama-3.1-sonar-small-128k-online, llama-3.1-sonar-large-128k-online, llama-3.1-sonar-huge-128k-online"
  }
}

Instalación

  1. Clonar el repositorio:

    git clone https://github.com/yourusername/perplexity-mcp-server.git
cd perplexity-mcp-server

  2. Instalar dependencias:

    npm install

  3. Crear archivo .env :

    PERPLEXITY_API_KEY=your-api-key-here

  4. Construir el proyecto:

    npm run build

Configuración del escritorio de Claude

Para agregar este servidor a Claude Desktop, actualice su claude_desktop_config.json :

{
  "mcpServers": {
    //more servers...
    "perplexity": {
      "command": "node",
      "args": ["path\\to\\perplexity-mcp-server\\dist\\index.js"],
      "env": {
        "PERPLEXITY_API_KEY": "YOUR_PERPLEXITY_API_KEY"
      }
    }
    //more servers...
  }
}

El archivo de configuración normalmente se encuentra en:

  • Ventanas: %APPDATA%/Claude/config/claude_desktop_config.json

  • macOS: ~/Library/Application Support/Claude/config/claude_desktop_config.json

  • Linux: ~/.config/Claude/config/claude_desktop_config.json

Desarrollo

Inicie el servidor de desarrollo con recompilación automática:

npm run dev

El servidor utiliza TypeScript e implementa el protocolo MCP utilizando el paquete @modelcontextprotocol/sdk .

Arquitectura

Componentes principales

  1. Clase PerplexityServer

    • Implementa el protocolo de servidor MCP

    • Maneja el registro y ejecución de herramientas.

    • Gestiona el manejo de errores y el ciclo de vida del servidor.

  2. Sistema de herramientas

    • Definiciones de herramientas modulares

    • Manipuladores de herramientas con seguridad de tipos

    • Validación de entrada estructurada

Detalles técnicos

  • Creado con TypeScript para seguridad de tipos

  • Utiliza @modelcontextprotocol/sdk para la implementación de MCP

  • Se comunica a través del transporte stdio

  • Configuración basada en el entorno

Manejo de errores

El servidor implementa un manejo integral de errores:

  • Informe de errores de API

  • Manejo de solicitudes de herramientas no válidas

  • Gestión de errores de conexión

  • Manejo de señales de proceso

Dependencias

  • @modelcontextprotocol/sdk : ^1.0.3

  • dotenv : ^16.4.7

  • isomorphic-fetch : ^3.0.0

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

Seguridad

  • Las claves API se gestionan a través de variables de entorno

  • Validación de entrada para todos los parámetros de la herramienta

  • Los mensajes de error se desinfectan antes de su salida.

  • Aislamiento de procesos mediante el protocolo MCP

Licencia

Este proyecto está licenciado bajo la licencia ISC.

Solución de problemas

Problemas comunes y soluciones:

  1. Servidor no encontrado

    • Verifique que la ruta en claude_desktop_config.json sea correcta

    • Asegúrese de que el servidor esté compilado ( npm run build )

    • Comprueba si Node.js está en tu PATH

  2. Errores de autenticación

    • Verifique su clave API de Perplexity en .env

    • Compruebe si la clave API tiene los permisos necesarios

  3. Errores de ejecución de herramientas

    • Verifique que los parámetros de la herramienta coincidan con el esquema

    • Comprobar la conectividad de la red

    • Revise los registros del servidor para ver mensajes de error detallados

Available Tools

2 tools
perplexity_askB

Send a simple query to Perplexity AI

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoThe model to use for completion
queryYesThe question or prompt to send

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations and a minimal description, the tool's behavior is opaque. It does not disclose whether the query is synchronous, what response format to expect, or any safety/reliability traits.

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 that immediately conveys the tool's purpose. It is appropriately front-loaded and concise, though it could incorporate more detail without becoming wordy.

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?

For a simple query tool with no output schema, the description lacks information about the response (e.g., text output, confidence scores). The agent would need to infer or discover the return type from 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?

Schema coverage is 100%, so both parameters have descriptions. The description adds no extra value beyond the schema, but baseline 3 is appropriate as the schema already documents parameters adequately.

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 ('Send a simple query') and the target resource ('Perplexity AI'). It distinguishes from the sibling tool 'perplexity_chat' by implying this is for a single query, while 'chat' likely involves multi-turn conversation.

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 the sibling 'perplexity_chat'. There is no mention of prerequisites, limitations, or alternatives, leaving the agent to infer usage context.

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

perplexity_chatC

Generate a chat completion using Perplexity AI

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoThe model to use for completion
messagesYesArray of messages in the conversation
temperatureNoSampling temperature (0-2)

TDQS

C2.8/5.0
Behavior2/5

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

No behavioral traits are disclosed beyond the minimal description. The schema includes a model with 'sonar-small-online' suggesting internet access, but this is not mentioned. With no annotations, the description should provide more context about side effects, permissions, or rate limits.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it is too brief to convey necessary information. It could be expanded without losing 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 has 3 parameters and a sibling tool, the description is insufficient. It does not explain the role of Perplexity AI, the meaning of different models, or how this tool differs from 'perplexity_ask'. The lack of output schema increases the need for a more complete description.

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%, so all parameters are documented in the schema. The description adds no additional meaning beyond what is already in the schema. 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 tool's purpose: generating a chat completion using Perplexity AI. However, it does not differentiate from the sibling tool 'perplexity_ask', which likely performs a similar but distinct function.

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 the sibling tool 'perplexity_ask'. The context hints at a distinction (chat vs. ask), but it is not explicitly stated.

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. 2 tool updatesv1.0.1
    • First observedperplexity_ask
    • First observedperplexity_chat
Deploy Server
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TDQS

C2.9/5.0

Scored across 2 tools

Disambiguation2/5

Both tools involve querying Perplexity AI, with only subtle differences in description ('simple query' vs 'chat completion'). An agent would likely struggle to decide which to use, as the boundaries are unclear.

Naming Consistency4/5

Both tools follow a consistent 'perplexity_<verb>' pattern. The verbs 'ask' and 'chat' are different but semantically related, and no mixing of naming conventions is present.

Tool Count3/5

With only 2 tools, the server feels minimal for a service like Perplexity AI, which typically offers more nuanced capabilities (e.g., different models, streaming). However, it covers basic query and chat needs.

Completeness3/5

The tool surface covers basic query and chat interactions but lacks operations such as specifying model parameters, context handling, or result streaming. This may limit agent flexibility.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

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