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DeepSeek MCP Server

A Model Context Protocol (MCP) server for the DeepSeek API, allowing seamless integration of DeepSeek's powerful language models with MCP-compatible applications like Claude Desktop.

Anonymously use DeepSeek API -- Only a proxy is seen on the other side

npm version npm downloads GitHub issues GitHub forks GitHub stars GitHub license

Related MCP server: Deepseek MCP Server

Installation

Installing via Smithery

To install DeepSeek MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @dmontgomery40/deepseek-mcp-server --client claude

Manual Installation

npm install -g deepseek-mcp-server

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "deepseek": {
      "command": "npx",
      "args": [
        "-y",
        "deepseek-mcp-server"
      ],
      "env": {
        "DEEPSEEK_API_KEY": "your-api-key"
      }
    }
  }
}

Features

Note: The server intelligently handles these natural language requests by mapping them to appropriate configuration changes. You can also query the current settings and available models:

  • User: "What models are available?"

    • Response: Shows list of available models and their capabilities via the models resource.

  • User: "What configuration options do I have?"

    • Response: Lists all available configuration options via the model-config resource.

  • User: "What is the current temperature setting?"

    • Response: Displays the current temperature setting.

  • User: "Start a multi-turn conversation. With the following settings: model: 'deepseek-chat', make it not too creative, and allow 8000 tokens."

    • Response: Starts a multi-turn conversation with the specified settings.

Automatic Model Fallback if R1 is down

  • If the primary model (R1) is down (called deepseek-reasoner in the server), the server will automatically attempt to try with v3 (called deepseek-chat in the server)

Note: You can switch back and forth anytime as well, by just giving your prompt and saying "use deepseek-reasoner" or "use deepseek-chat"

  • V3 is recommended for general purpose use, while R1 is recommended for more technical and complex queries, primarily due to speed and token usage

Resource discovery for available models and configurations:

  • Custom model selection

  • Temperature control (0.0 - 2.0)

  • Max tokens limit

  • Top P sampling (0.0 - 1.0)

  • Presence penalty (-2.0 - 2.0)

  • Frequency penalty (-2.0 - 2.0)

Enhanced Conversation Features

Multi-turn conversation support:

  • Maintains complete message history and context across exchanges

  • Preserves configuration settings throughout the conversation

  • Handles complex dialogue flows and follow-up chains automatically

This feature is particularly valuable for two key use cases:

  1. Training & Fine-tuning: Since DeepSeek is open source, many users are training their own versions. The multi-turn support provides properly formatted conversation data that's essential for training high-quality dialogue models.

  2. Complex Interactions: For production use, this helps manage longer conversations where context is crucial:

    • Multi-step reasoning problems

    • Interactive troubleshooting sessions

    • Detailed technical discussions

    • Any scenario where context from earlier messages impacts later responses

The implementation handles all context management and message formatting behind the scenes, letting you focus on the actual interaction rather than the technical details of maintaining conversation state.

Testing with MCP Inspector

You can test the server locally using the MCP Inspector tool:

  1. Build the server:

    npm run build
  2. Run the server with MCP Inspector:

    # Make sure to specify the full path to the built server
    npx @modelcontextprotocol/inspector node ./build/index.js

The inspector will open in your browser and connect to the server via stdio transport. You can:

  • View available tools

  • Test chat completions with different parameters

  • Debug server responses

  • Monitor server performance

Note: The server uses DeepSeek's R1 model (deepseek-reasoner) by default, which provides state-of-the-art performance for reasoning and general tasks.

License

MIT

Available Tools

2 tools
chat_completionD
ParametersJSON Schema
NameRequiredDescriptionDefault
frequency_penaltyNo
max_tokensNo
messageNo
messagesNo
modelNodeepseek-reasoner
presence_penaltyNo
temperatureNo
top_pNo

TDQS

D1/5.0
Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

multi_turn_chatD
ParametersJSON Schema
NameRequiredDescriptionDefault
frequency_penaltyNo
max_tokensNo
messagesYes
modelNodeepseek-chat
presence_penaltyNo
temperatureNo
top_pNo

TDQS

D1/5.0
Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedchat_completion
    • First observedmulti_turn_chat

TDQS

D1.5/5.0
Disambiguation2/5

The two tools have overlapping purposes with unclear boundaries. 'chat_completion' and 'multi_turn_chat' both appear to handle chat interactions, making it difficult for an agent to distinguish when to use each. Without descriptions, the ambiguity is significant, though the small tool count limits confusion.

Naming Consistency4/5

The tool names follow a consistent snake_case pattern with clear, descriptive terms. Both 'chat_completion' and 'multi_turn_chat' use similar naming conventions, making them readable and predictable, though the lack of descriptions reduces clarity.

Tool Count2/5

With only 2 tools, the server feels thin and under-scoped for a general-purpose 'DeepSeek MCP Server'. This minimal set suggests incomplete coverage of potential functionalities, such as model management or configuration, making it borderline inadequate.

Completeness2/5

Inferring the domain as AI chat interactions, the surface is severely incomplete. It lacks essential operations like model selection, parameter tuning, or context management. The two tools provide basic chat functions but leave significant gaps that could cause agent failures in complex tasks.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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