Perplexity MCP Server
Servidor MCP perplexity-mcp
Un servidor de Protocolo de Contexto de Modelo (MCP) que proporciona funcionalidad de búsqueda web mediante la API de Perplexity AI . Funciona con el cliente de escritorio Anthropic Claude.
Ejemplo
Le permite utilizar indicaciones como: "Busque en la web para descubrir qué novedades hubo en Anthropic durante la última semana".
Related MCP server: Perplexity MCP Server
Puntuaciones de Glama
Componentes
Indicaciones
El servidor proporciona un único mensaje:
perplexity_search_web: Busca en la web usando Perplexity AI
Argumento "consulta" obligatorio para la consulta de búsqueda
Argumento opcional de "actualidad" para filtrar los resultados por período de tiempo:
'día': últimas 24 horas
'semana': últimos 7 días
'mes': últimos 30 días (predeterminado)
'año': últimos 365 días
Utiliza la API de Perplexity para realizar búsquedas web
Herramientas
El servidor implementa una herramienta:
perplexity_search_web: Busca en la web usando Perplexity AI
Toma "consulta" como un argumento de cadena obligatorio
Parámetro opcional de "actualidad" para filtrar resultados (día/semana/mes/año)
Devuelve resultados de búsqueda de la API de Perplexity
Instalación
Instalación mediante herrería
Para instalar Perplexity MCP para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install perplexity-mcp --client claudeRequiere UV (paquete Fast Python y administrador de proyectos)
Si uv no está instalado.
# Using Homebrew on macOS
brew install uvo
# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh
# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Variables de entorno
La siguiente variable de entorno es necesaria en su archivo claude_desktop_config.json. Puede obtener una clave API de Perplexity.
PERPLEXITY_API_KEY: Su clave API de Perplexity AI
Variables de entorno opcionales:
PERPLEXITY_MODEL: El modelo de perplejidad a utilizar (el valor predeterminado es "sonar" si no se especifica)Modelos disponibles:
sonar-deep-research: contexto de 128k - Capacidades de investigación mejoradassonar-reasoning-pro: contexto de 128k: razonamiento avanzado con enfoque profesionalsonar-reasoning: contexto de 128k - Capacidades de razonamiento mejoradassonar-pro: contexto de 200k - Modelo de nivel profesionalsonar: contexto 128k - Modelo predeterminador1-1776: contexto de 128k - Arquitectura alternativa
Y la lista actualizada de modelos está disponible (aquí) [ https://docs.perplexity.ai/guides/model-cards ]
Instalación de Cursor y Claude Desktop
Agregue esta herramienta como servidor mcp editando el archivo de configuración Cursor/Claude.
"perplexity-mcp": {
"env": {
"PERPLEXITY_API_KEY": "XXXXXXXXXXXXXXXXXXXX",
"PERPLEXITY_MODEL": "sonar"
},
"command": "uvx",
"args": [
"perplexity-mcp"
]
}Cursor
En MacOS:
/Users/your-username/.cursor/mcp.jsonEn Windows:
C:\Users\your-username\.cursor\mcp.json
Si todo funciona correctamente, ahora deberías poder llamar a la herramienta desde Cursor.
Escritorio de Claude
En MacOS:
~/Library/Application\ Support/Claude/claude_desktop_config.jsonEn Windows:
%APPDATA%/Claude/claude_desktop_config.json
Para verificar el funcionamiento del servidor, abra el cliente de Claude y use una opción como "Buscar noticias sobre OpenAI en la web la semana pasada". Debería aparecer un cuadro de alerta para confirmar el uso de la herramienta. Haga clic en "Permitir este chat".
Available Tools
1 toolperplexity_search_webC
Search the web using Perplexity AI with recency filtering
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| recency | No | month |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility for behavioral disclosure. It does not mention whether the operation is read-only, any rate limits, authentication requirements, or side effects. The tool is likely a safe read operation but this is not confirmed, leaving the agent without critical behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that efficiently conveys the core purpose. No redundant information is present. However, it could be slightly expanded to include more context without losing conciseness, hence a 4 rather than 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, no annotations, and two parameters with no schema descriptions, the description is severely lacking. It does not specify what the search returns (type of content, structure), any usage limits, or prerequisites. An agent cannot reliably determine if this tool is appropriate for a given task without additional external knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no parameter descriptions. The description adds only 'with recency filtering' which hints at the recency parameter but does not explain the query parameter or the meaning of recency enum values (day, week, month, year). This is insufficient for an agent to form correct parameter values without additional knowledge.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('search'), the resource ('web'), the specific service ('Perplexity AI'), and includes a key feature ('recency filtering'). This is specific and unambiguous, avoiding tautology by adding value beyond the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 or when to consider alternatives. Since there are no sibling tools, the lack of usage context is less critical, but the description could still mention typical use cases (e.g., 'for up-to-date web results with time constraints'). Currently, it merely rephrases the tool name.
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 tool update
v1.0.0- First observed
perplexity_search_web
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose that cannot be confused with any other tool in the set.
The single tool name follows a consistent verb_noun pattern (perplexity_search_web), and with only one tool, there is no inconsistency to evaluate. The naming is clear and descriptive.
One tool is too few for a server with a broad purpose like web search, as it lacks related operations such as filtering results, getting details, or handling different search types. This minimal set may limit agent functionality in practical scenarios.
The server is severely incomplete for web search functionality, offering only a basic search tool without capabilities like refining queries, paginating results, or accessing cached or specific types of content. This creates significant gaps for agents trying to perform comprehensive web searches.
Maintenance
Related MCP Connectors
Real-time web search, reasoning, and research through Perplexity's API
Enable AI assistants to perform web searches using Perplexity's Sonar Pro.
Web search, agentic search, news, page retrieval, sitemaps, and trending topics through Search1API.
MCP server for Google search results via SERP API
Related MCP Servers
- AlicenseBqualityDmaintenanceThis server allows users to perform web searches using Perplexity AI, providing a tool for retrieving search results through a simple API interface.1120 npm3Apache 2.0
- AlicenseBqualityDmaintenanceFacilitates web search capabilities using Perplexity's API, allowing users to retrieve search results through Claude's interface.18MIT
- AlicenseAqualityDmaintenanceAn MCP server that enables Claude to perform web searches using Perplexity's API with intelligent model selection based on query intent and support for domain and recency filtering.64MIT
- AlicenseBqualityDmaintenanceA server that integrates with Claude Desktop to enable real-time web research capabilities, allowing users to search Google, extract webpage content, and capture screenshots directly from conversations.31,766 npmMIT