Un potente asistente de investigación que se integra con Cline y Claude Desktop. Aprovecha Perplexity AI para búsqueda inteligente, recuperación de documentación, descubrimiento de API y asistencia para la modernización de código, todo mientras escribes código.
Seguimiento de contexto continuo : mantiene el historial de conversaciones en la base de datos SQLite para proporcionar respuestas coherentes en múltiples consultas
Procesamiento avanzado de consultas : utiliza los modelos Sonar de Perplexity para un razonamiento sofisticado y respuestas detalladas a preguntas complejas.
Gestión de velocidad inteligente : implementa una limitación de velocidad adaptativa con retroceso exponencial para maximizar el uso de la API sin alcanzar los límites
Redes de alto rendimiento : optimiza las llamadas API con agrupación de conexiones y lógica de reintento automático para un funcionamiento confiable
Related MCP server: Perplexity Tool for Claude Desktop
Realiza búsquedas generales para obtener información completa sobre cualquier tema. El ejemplo muestra cómo usar diferentes niveles de detalle (breve, normal, detallado) para obtener respuestas personalizadas.
Recupera documentación y ejemplos de uso para tecnologías, bibliotecas o API específicas. El ejemplo muestra cómo obtener documentación completa para los hooks de React, incluyendo las mejores prácticas y los errores más comunes.
Descubre y evalúa las API que podrían integrarse en un proyecto. El ejemplo muestra cómo encontrar API de procesamiento de pagos con un análisis detallado de sus características, precios y complejidad de integración.
Analiza el código en busca de características o patrones obsoletos y proporciona orientación para la migración. El ejemplo muestra cómo comprobar los componentes de clase y los métodos del ciclo de vida de React para encontrar alternativas modernas.
Primero instale Node.js si aún no está instalado (desde nodejs.org)
Clonar el repositorio
Instalar dependencias y compilar
Obtenga una clave API de Perplexity desde https://www.perplexity.ai/settings/api
Cree el archivo de configuración MCP en la ubicación adecuada para su sistema operativo:
Para utilizar con Claude Desktop, agregue la configuración del servidor:
Para usar con Cline, agregue a mcpServers:
{
"mcpServers": {
"perplexity-server": {
"command": "node",
"args": ["[path/to/researcher-mcp/build/index.js]"],
"env": {
"PERPLEXITY_API_KEY": "pplx-...",
"PERPLEXITY_MODEL": "sonar-reasoning" // you can use different models
},
"disabled": false,
"alwaysAllow": [],
"autoApprove": [
"search",
"get_documentation",
"find_apis",
"check_deprecated_code",
"get_request_status"
]
}
}
}Construya el servidor: npm run build
Check if code or dependencies might be using deprecated features
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code snippet or dependency to check | |
| technology | No | The technology or framework context (e.g., 'React', 'Node.js') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose behavioral traits beyond its basic function. It does not state if the check is read-only, whether it executes the code, what input format is expected, or what output is produced. With no annotations, this lack of detail is a significant gap.
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, focused sentence with no redundant information. It gets straight to the point.
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?
Despite the small parameter count, the description lacks crucial information about the tool's behavior and return value. Without an output schema or annotations, the agent is left without a clear picture of what to expect. The description is too minimal to be complete.
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?
The schema fully describes both parameters (code and technology), so the description doesn't need to elaborate. The description adds no additional parameter context beyond the schema, aligning with the baseline score of 3.
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 tool's function: checking code or dependencies for deprecated features. It distinguishes from sibling tools like search and get_documentation by focusing on deprecation analysis rather than general information retrieval.
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 versus alternatives like search or get_documentation. The description only states what it does, implying usage but offering no exclusions or comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find and evaluate APIs that could be integrated into a project
| Name | Required | Description | Default |
|---|---|---|---|
| requirement | Yes | The functionality or requirement you're looking to fulfill | |
| context | No | Additional context about the project or specific needs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only states the purpose. It doesn't mention whether the tool performs live searches, what evaluation criteria are used, what output format is returned, or any side effects. This is a significant lack of transparency.
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 is front-loaded and directly conveys the tool's purpose. Every word earns its place, and there is no unnecessary detail or fluff.
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?
Given the lack of annotations and output schema, the description should compensate by explaining return values, research process, or usage context. It does none of this, leaving the agent without a clear picture of the tool's output, behavior, or when to choose it over siblings.
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?
The input schema already describes both parameters (requirement and context) with 100% coverage, so the baseline is 3. The tool description adds no additional meaning beyond the schema; it doesn't explain how the parameters influence behavior or results.
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 tool finds and evaluates APIs for project integration, using a specific verb and resource. It distinguishes itself from sibling tools like general search and get_documentation, though it doesn't detail the evaluation criteria or depth.
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 gives no guidance on when to use this tool versus alternatives. It doesn't mention any exclusions, prerequisites, or comparisons with sibling tools like search or get_documentation, leaving the appropriate usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get documentation and usage examples for a specific technology, library, or API
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The technology, library, or API to get documentation for | |
| context | No | Additional context or specific aspects to focus on |
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 only restates the primary function and reveals nothing about potential side effects, output format, source reliability, or limitations. The agent is left guessing about what 'documentation' entails (e.g., official docs, community examples, version specifics).
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 front-loads the main verb and resource. There is no wasted wording or unnecessary detail.
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 simple tool with full schema coverage and no output schema, the description is minimally viable but lacks usage guidance and behavioral context. It could be more complete by explaining what types of documentation are returned or how it differs from sibling tools.
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 coverage is 100% for both parameters, so the baseline is 3. The description adds no additional meaning beyond what the schema already provides; it merely reiterates 'specific technology, library, or API' which matches the 'query' parameter description.
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 verb ('Get') and the resource ('documentation and usage examples') for a 'specific technology, library, or API.' This is specific and distinguishes it from generic search, though it doesn't explicitly call out sibling tools.
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 given on when to use this tool versus alternatives like search, find_apis, or chat_perplexity. The description implies a documentation-focused purpose but provides no exclusions, prerequisites, or contextual cues for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform a general search query to get comprehensive information on any topic
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query or question | |
| detail_level | No | Optional: Desired level of detail (brief, normal, detailed) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It only promises 'comprehensive information' without noting whether the tool is read-only, what data source it accesses, whether it has rate limits, or what the response format looks like.
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 one-sentence description is efficient and front-loaded with the verb and resource. It contains no filler, though its brevity limits the amount of actionable guidance.
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?
Given the simple schema and lack of an output schema, the description still leaves important gaps: it does not explain what kind of results are returned, when this tool should be preferred over sibling tools, or how the optional detail_level affects behavior.
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?
The input schema already documents both parameters with descriptions, giving 100% coverage. The description adds only generic context ('any topic') and does not enrich the meaning of the parameters beyond what the schema provides.
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 states a clear action ('Perform a general search query') and resource ('search') to gather information. However, it does not differentiate this from sibling tools like get_documentation or find_apis, so it stops short of full clarity.
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 choose this tool over alternatives. The phrase 'any topic' is extremely broad and gives no context or exclusions, leaving the agent without direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
check_deprecated_codefind_apisget_documentationsearchScored across 4 tools
Each tool has a clearly distinct purpose with no overlap: checking deprecated code, finding APIs, getting documentation, and performing general searches. The descriptions make it easy for an agent to select the right tool for each specific research task.
Three tools follow a consistent verb_noun pattern (check_deprecated_code, find_apis, get_documentation), but 'search' deviates as a single verb without an object. This minor inconsistency slightly affects predictability, though all names remain readable.
With only 4 tools, the set feels thin for a research server that aims to cover broad information-gathering tasks. While each tool is useful, the scope suggests more specialized research operations could be missing, making it borderline appropriate.
The tools cover key research functions like checking deprecations, finding APIs, getting docs, and general searches, but there are notable gaps. For example, missing tools for comparing technologies, validating information sources, or tracking research progress limit comprehensive workflow coverage.
Real-time web search, reasoning, and research through Perplexity's API
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Enable AI assistants to perform web searches using Perplexity's Sonar Pro.
