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Deepseek R1 MCP Server

by kamelirzouni
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JavaScript
Hybrid

Deepseek R1 MCP Server

A Model Context Protocol (MCP) server implementation for the Deepseek R1 language model. Deepseek R1 is a powerful language model optimized for reasoning tasks with a context window of 8192 tokens.

Why Node.js? This implementation uses Node.js/TypeScript as it provides the most stable integration with MCP servers. The Node.js SDK offers better type safety, error handling, and compatibility with Claude Desktop.

Quick Start

Installing manually

# 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

Prerequisites

  • Node.js (v18 or higher)

  • npm

  • Claude Desktop

  • Deepseek API key

Model Selection

By default, this server uses the deepseek-R1 model. If you want to use DeepSeek-V3 instead, modify the model name in src/index.ts:

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

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

Project Structure

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

Configuration

  1. Create a .env file:

DEEPSEEK_API_KEY=your-api-key-here

  1. Update Claude Desktop configuration:

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

Development

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

Features

  • Advanced text generation with Deepseek R1 (8192 token context window)

  • Configurable parameters (max_tokens, temperature)

  • Robust error handling with detailed error messages

  • Full MCP protocol support

  • Claude Desktop integration

  • Support for both DeepSeek-R1 and DeepSeek-V3 models

API Usage

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

The Temperature Parameter

The default value of temperature is 0.2.

Deepseek recommends setting the temperature according to your specific use case:

USE CASE

TEMPERATURE

EXAMPLE

Coding / Math

0.0

Code generation, mathematical calculations

Data Cleaning / Data Analysis

1.0

Data processing tasks

General Conversation

1.3

Chat and dialogue

Translation

1.3

Language translation

Creative Writing / Poetry

1.5

Story writing, poetry generation

Error Handling

The server provides detailed error messages for common issues:

  • API authentication errors

  • Invalid parameters

  • Rate limiting

  • Network issues

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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.

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TDQS

B3.1/5.0
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
ResponsivenessSyncing

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