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Deepseek R1 MCP Server

by kamelirzouni
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JavaScript
Hybrid

Servidor MCP Deepseek R1

Implementación de servidor del Protocolo de Contexto de Modelo (MCP) para el modelo de lenguaje Deepseek R1. Deepseek R1 es un potente modelo de lenguaje optimizado para tareas de razonamiento con una ventana de contexto de 8192 tokens.

¿Por qué Node.js? Esta implementación utiliza Node.js/TypeScript, ya que proporciona la integración más estable con servidores MCP. El SDK de Node.js ofrece mayor seguridad de tipos, gestión de errores y compatibilidad con Claude Desktop.

Inicio rápido

Instalación manual

# Clone and install
git clone https://github.com/66julienmartin/MCP-server-Deepseek_R1.git
cd deepseek-r1-mcp
npm install

# Set up environment
cp .env.example .env  # Then add your API key

# Build and run
npm run build

Related MCP server: MCP Advanced Reasoning Server

Prerrequisitos

  • Node.js (v18 o superior)

  • npm

  • Escritorio de Claude

  • Clave API de Deepseek

Selección de modelos

De forma predeterminada, este servidor utiliza el modelo deepseek-R1 . Si prefiere usar DeepSeek-V3 , modifique el nombre del modelo en src/index.ts :

// For DeepSeek-R1 (default)
model: "deepseek-reasoner"

// For DeepSeek-V3
model: "deepseek-chat"

Estructura del proyecto

deepseek-r1-mcp/
├── src/
│   ├── index.ts             # Main server implementation
├── build/                   # Compiled files
│   ├── index.js
├── LICENSE
├── README.md
├── package.json
├── package-lock.json
└── tsconfig.json

Configuración

  1. Crea un archivo .env :

DEEPSEEK_API_KEY=your-api-key-here

  1. Actualizar la configuración de Claude Desktop:

{
  "mcpServers": {
    "deepseek_r1": {
      "command": "node",
      "args": ["/path/to/deepseek-r1-mcp/build/index.js"],
      "env": {
        "DEEPSEEK_API_KEY": "your-api-key"
      }
    }
  }
}

Desarrollo

npm run dev     # Watch mode
npm run build   # Build for production

Características

  • Generación avanzada de texto con Deepseek R1 (ventana de contexto de token 8192)

  • Parámetros configurables (max_tokens, temperatura)

  • Manejo robusto de errores con mensajes de error detallados

  • Compatibilidad total con el protocolo MCP

  • Integración de Claude Desktop

  • Compatibilidad con los modelos DeepSeek-R1 y DeepSeek-V3

Uso de la API

{
  "name": "deepseek_r1",
  "arguments": {
    "prompt": "Your prompt here",
    "max_tokens": 8192,    // Maximum tokens to generate
    "temperature": 0.2     // Controls randomness
  }
}

El parámetro de temperatura

El valor predeterminado de temperature es 0,2.

Deepseek recomienda configurar la temperature según su caso de uso específico:

CASO DE USO

TEMPERATURA

EJEMPLO

Codificación / Matemáticas

0.0

Generación de código, cálculos matemáticos

Limpieza de datos / Análisis de datos

1.0

Tareas de procesamiento de datos

Conversación general

1.3

Chat y diálogo

Traducción

1.3

Traducción de idiomas

Escritura creativa / poesía

1.5

Escritura de cuentos, generación de poesía

Manejo de errores

El servidor proporciona mensajes de error detallados para problemas comunes:

  • Errores de autenticación de API

  • Parámetros no válidos

  • Limitación de velocidad

  • Problemas de red

Contribuyendo

¡Agradecemos sus contribuciones! No dude en enviar una solicitud de incorporación de cambios.

Licencia

Instituto Tecnológico de Massachusetts (MIT)

Available Tools

1 tool
deepseek_r1C

Generate text using DeepSeek R1 model

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesInput text for DeepSeek
max_tokensNoMaximum tokens to generate (default: 8192)
temperatureNoSampling temperature (default: 0.2)

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Generate text' implies a read/write operation that creates content, it lacks critical behavioral details such as rate limits, authentication requirements, response format, error conditions, or whether it's idempotent. The description adds minimal value beyond the basic function.

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

Conciseness5/5

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

The description is extremely concise with a single, clear sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration, making it efficiently front-loaded and easy to parse.

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 text generation tool with 3 parameters and no output schema, the description is insufficiently complete. It doesn't explain what kind of text is generated, typical use cases, limitations, or what the return value looks like. The combination of no annotations and no output schema means the description should provide more contextual information than it does.

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?

The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline expectation but doesn't provide extra semantic context.

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 action ('Generate text') and specifies the resource ('using DeepSeek R1 model'), which provides a specific verb+resource combination. However, since there are no sibling tools mentioned, there's no opportunity to distinguish from alternatives, preventing a perfect score of 5.

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 alternatives, prerequisites, or contextual constraints. It simply states what the tool does without any usage instructions, which is insufficient for effective tool selection.

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 observeddeepseek_r1
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TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as text generation using the DeepSeek R1 model, leaving no ambiguity for an agent to misselect between multiple options.

Naming Consistency5/5

A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The tool name 'deepseek_r1' follows a clear pattern that matches the server name and describes its function, with no deviations or mixed conventions present.

Tool Count2/5

One tool is too few for a server's apparent scope, as it suggests minimal functionality that might not support complex workflows. While a single tool can be appropriate for very narrow purposes, this server's name implies a broader capability that a single text generation tool does not fully cover, making it feel thin and limited.

Completeness2/5

The tool surface is severely incomplete for the server's implied domain of DeepSeek R1 model interactions. It only offers text generation, lacking obvious gaps such as model configuration, parameter tuning, or other common AI model operations like embeddings or fine-tuning, which could cause agent failures in broader tasks.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

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