Skip to main content
Glama
enesjakozh
moyu6027

DeepSeek MCP Server

by moyu6027
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Servidor MCP de DeepSeek

Servidor MCP de DeepSeek

🚀 Características

Mejore las capacidades de razonamiento de Claude con la integración del motor de razonamiento avanzado de DeepSeek R1. Este servidor le permite abordar tareas de razonamiento complejas aprovechando las capacidades de razonamiento del modelo DeepSeek R1.

  • DeepSeek R1 (El Cerebro) actúa como un planificador de razonamiento avanzado:

    • Planifica estrategias de análisis lógico de varios pasos

    • Estructura los marcos cognitivos

    • Evalúa la confianza y la incertidumbre

    • Monitorea la calidad del razonamiento

    • Detecta casos extremos y sesgos

  • Claude (El Ejecutor) implementa los planes de razonamiento:

    • Ejecuta el análisis estructurado

    • Implementa estrategias planificadas

    • Entrega respuestas finales

    • Maneja la interacción del usuario

    • Gestiona integraciones de sistemas

Related MCP server: Deepseek R1 MCP Server

🚀 Características

Capacidades de razonamiento avanzadas

  • Admite tareas complejas de razonamiento de varios pasos.

  • Diseñado para brindar precisión y eficiencia a la hora de generar respuestas bien pensadas.

  • API de la plataforma de desarrollo de software

Guía de instalación completa

Prerrequisitos

  1. Clonar el repositorio

    git clone https://github.com/moyu6027/deepseek-MCP-server.git
cd deepseek-MCP-server

  2. Asegúrese de que la luz UV esté configurada

    • Windows : ejecute lo siguiente en PowerShell:

      powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

    • Mac : Ejecute lo siguiente:

      curl -LsSf https://astral.sh/uv/install.sh | sh

  3. Crear entorno virtual

    uv venv
source .venv/bin/activate

  4. Instalar dependencias

    uv add "mcp[cli]" httpx

  5. Configurar la clave API

    echo "INFINI_API_KEY=your_key_here" > .env

  6. Instalar el servidor

    mcp install server.py -f .env

  7. Configurar el servidor MCP Edite el archivo claude_desktop_config.json para incluir la siguiente configuración:

    {
    "mcpServers": {
        "deepseek-mcp": {
            "command": "uv",
            "args": [
                "--directory",
                "PATH_TO_DEEPSEEK_MCP_SERVER",
                "run",
                "server.py"
            ]
        }
    }
}

  8. Ejecutar el servidor

    uv run server.py

🛠 Uso

Iniciando el servidor

El servidor se inicia automáticamente al usar Claude Desktop. Asegúrese de que Claude Desktop esté configurado para detectar el servidor MCP.

Ejemplo de flujo de trabajo

  1. Claude recibe una consulta que requiere un razonamiento avanzado.

  2. La consulta se envía a DeepSeek R1 para su procesamiento.

  3. DeepSeek R1 devuelve un razonamiento estructurado envuelto en etiquetas <ant_thinking> .

  4. Claude integra el razonamiento en su respuesta final.

📄 Licencia

Este proyecto está licenciado bajo la Licencia MIT. Consulte el archivo de LICENCIA para más detalles.

Available Tools

1 tool
reasonB
Process a query using DeepSeek's R1 reasoning engine and prepare it for integration with DeepSeek V3 or claude.

DeepSeek R1 leverages advanced reasoning capabilities that naturally evolved from large-scale 
reinforcement learning, enabling sophisticated reasoning behaviors. The output is enclosed 
within `<ant_thinking>` tags to align with V3 or Claude's thought processing framework.

Args:
    query (dict): Contains the following keys:
        - context (str): Optional background information for the query.
        - question (str): The specific question to be analyzed.

Returns:
    str: The reasoning output from DeepSeek, formatted with `<ant_thinking>` tags for seamless use with V3 or Claude.
ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.4/5.0
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. It mentions the tool 'leverages advanced reasoning capabilities' and outputs formatted text, but fails to disclose critical behavioral traits such as rate limits, error handling, authentication requirements, or performance characteristics. The description adds some context about the reasoning engine but leaves significant gaps for a tool with potential computational costs.

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 appropriately sized and front-loaded, with the core purpose stated in the first sentence. Additional sentences provide useful context about the reasoning engine and output formatting. There is minor redundancy in mentioning 'V3 or Claude' twice, but overall, it's efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (1 parameter with nested structure), no annotations, and an output schema that exists (though not detailed here), the description is reasonably complete. It explains the purpose, parameter semantics, and output format, though it could improve by addressing behavioral aspects like error cases or integration specifics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds substantial meaning beyond the input schema, which has 0% description coverage and only specifies a generic object. It details that the 'query' parameter is a dict with 'context' (optional background) and 'question' (specific question) keys, clarifying the expected structure and semantics. This compensates well for the schema's lack of documentation.

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: 'Process a query using DeepSeek's R1 reasoning engine and prepare it for integration with DeepSeek V3 or claude.' It specifies the verb ('process'), resource ('query'), and technology ('DeepSeek's R1 reasoning engine'), but since there are no sibling tools, it cannot demonstrate differentiation from alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by mentioning integration with 'V3 or Claude's thought processing framework,' suggesting it's for preparing reasoning outputs for those systems. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., direct API calls or other reasoning engines) and does not specify prerequisites or exclusions.

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. 1 tool updatev1.0.0
    • First observedreason
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3.4/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool named 'reason', there is no possibility of ambiguity or overlap with other tools. The tool has a single, clearly defined purpose of processing queries through DeepSeek's reasoning engine.

Naming Consistency5/5

A single tool cannot demonstrate inconsistency in naming patterns. The tool name 'reason' follows a clear verb-based convention that directly describes its function.

Tool Count2/5

One tool is too few for a server that appears to interface with a complex reasoning engine. While the tool is well-described, a single tool surface severely limits the server's capabilities and suggests an incomplete implementation for the domain.

Completeness2/5

The server has a significant gap in functionality. A reasoning engine server should offer more than just query processing - there are no tools for configuration, status checking, result formatting options, or other typical operations expected from such a service.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

Appeared in Searches