MCP Perplexity Search
búsqueda de perplejidad mcp
⚠️ Aviso
Este repositorio ya no se mantiene.
La funcionalidad de esta herramienta ahora está disponible en mcp-omnisearch , que combina múltiples herramientas MCP en un paquete unificado.
Utilice mcp-omnisearch en su lugar.
Un servidor de Protocolo de Contexto de Modelo (MCP) para integrar la API de IA de Perplexity con LLM. Este servidor ofrece funciones avanzadas de finalización de chat con plantillas de mensajes especializados para diversos casos de uso.
Related MCP server: DocGen MCP Server
Características
🤖 Finalización avanzada de chat usando los modelos de IA de Perplexity
📝 Plantillas de indicaciones predefinidas para escenarios comunes:
Generación de documentación técnica
Análisis de las mejores prácticas de seguridad
Revisión y mejoras del código
Documentación de la API en formato estructurado
🎯 Soporte de plantillas personalizadas para casos de uso especializados
📊 Múltiples formatos de salida (texto, markdown, JSON)
🔍 Inclusión opcional de URL de origen en las respuestas
⚙️ Parámetros del modelo configurables (temperatura, tokens máximos)
🚀 Soporte para varios modelos de Perplexity, incluidos Sonar y LLaMA
Configuración
Este servidor requiere configuración a través de su cliente MCP. A continuación, se muestran ejemplos para diferentes entornos:
Configuración de Cline
Agregue esto a su configuración de Cline MCP:
{
"mcpServers": {
"mcp-perplexity-search": {
"command": "npx",
"args": ["-y", "mcp-perplexity-search"],
"env": {
"PERPLEXITY_API_KEY": "your-perplexity-api-key"
}
}
}
}Escritorio Claude con configuración WSL
Para entornos WSL, agregue esto a su configuración de Claude Desktop:
{
"mcpServers": {
"mcp-perplexity-search": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"source ~/.nvm/nvm.sh && PERPLEXITY_API_KEY=your-perplexity-api-key /home/username/.nvm/versions/node/v20.12.1/bin/npx mcp-perplexity-search"
]
}
}
}Variables de entorno
El servidor requiere la siguiente variable de entorno:
PERPLEXITY_API_KEY: Su clave API de Perplexity (obligatoria)
API
El servidor implementa una única herramienta MCP con parámetros configurables:
finalización del chat
Genere finalizaciones de chat utilizando la API de Perplexity con soporte para plantillas de indicaciones especializadas.
Parámetros:
messages(matriz, obligatorio): Matriz de objetos de mensaje con:role(cadena): 'sistema', 'usuario' o 'asistente'content(cadena): El contenido del mensaje
prompt_template(cadena, opcional): Plantilla predefinida para utilizar:technical_docs: Documentación técnica con ejemplos de códigosecurity_practices: Pautas de implementación de seguridadcode_review: Análisis y mejoras del códigoapi_docs: documentación de la API en formato JSON
custom_template(objeto, opcional): Plantilla de mensaje personalizada con:system(cadena): Mensaje del sistema para el comportamiento del asistenteformat(cadena): preferencia de formato de salidainclude_sources(booleano): si se deben incluir fuentes
format(cadena, opcional): 'texto', 'markdown' o 'json' (predeterminado: 'texto')include_sources(booleano, opcional): incluye URL de origen (valor predeterminado: falso)model(cadena, opcional): modelo de perplejidad a utilizar (predeterminado: 'sonar')temperature(número, opcional): Aleatoriedad de salida (0-1, predeterminado: 0,7)max_tokens(número, opcional): longitud máxima de respuesta (predeterminado: 1024)
Desarrollo
Configuración
Clonar el repositorio
Instalar dependencias:
pnpm installConstruir el proyecto:
pnpm buildEjecutar en modo de desarrollo:
pnpm devPublicación
El proyecto utiliza conjuntos de cambios para la gestión de versiones. Para publicar:
Crear un conjunto de cambios:
pnpm changesetVersionar el paquete:
pnpm changeset versionPublicar en npm:
pnpm releaseContribuyendo
¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.
Licencia
Licencia MIT: consulte el archivo LICENCIA para obtener más detalles.
Expresiones de gratitud
Construido sobre el Protocolo de Contexto Modelo
Desarrollado por Perplexity SONAR
Available Tools
1 toolchat_completionC
Generate chat completions using the Perplexity API
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | ||
| prompt_template | No | Predefined prompt template to use for common use cases. Available templates: - technical_docs: Technical documentation with code examples and source references - security_practices: Security best practices and implementation guidelines with references - code_review: Code analysis focusing on best practices and improvements - api_docs: API documentation in structured JSON format with examples | |
| custom_template | No | Custom prompt template. If provided, overrides prompt_template. | |
| format | No | Response format. Use json for structured data, markdown for formatted text with code blocks. Overrides template format if provided. | text |
| include_sources | No | Include source URLs in the response. Overrides template setting if provided. | |
| model | No | Model to use for completion. Note: llama-3.1 models will be deprecated after 2/22/2025 | sonar |
| temperature | No | Controls randomness in the output. Higher values (e.g. 0.8) make the output more random, while lower values (e.g. 0.2) make it more focused and deterministic. | |
| max_tokens | No | The maximum number of tokens to generate in the response. One token is roughly 4 characters for English text. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic function. It doesn't mention rate limits, authentication requirements, cost implications, error handling, or response characteristics. For a complex API tool with 8 parameters, this is inadequate.
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, efficient sentence with zero wasted words. It's appropriately sized and gets straight to the point without unnecessary elaboration.
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 complex chat completion tool with 8 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what a 'chat completion' entails, typical use cases, or what the response looks like. The agent must rely entirely on the schema for operational details.
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?
With 88% schema description coverage, the schema already documents most parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline for high coverage but doesn't provide additional semantic context.
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 ('generate chat completions') and target ('using the Perplexity API'), providing a specific verb+resource combination. However, with no sibling tools mentioned, 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.
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 typical use cases. It simply states what the tool does without context about appropriate scenarios or limitations.
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
chat_completion
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined as generating chat completions using the Perplexity API, leaving no room for misselection.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'chat_completion' follows a clear verb_noun pattern, and there are no other tools to compare or create inconsistencies with.
A single tool is generally too few for a server's purpose, as it limits functionality and may indicate an incomplete or overly narrow scope. While it could be appropriate for a very simple service, it often feels thin and lacks the breadth needed for typical agent workflows.
The tool provides a core function for chat completions, but with only one tool, the surface is notably incomplete. There are obvious gaps, such as missing operations for managing conversations, handling different models, or supporting related search functionalities, which could hinder agent performance in broader tasks.
Maintenance
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