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DeepSeek-Claude MCP Server

by HarshJ23
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Python
Hybrid

DeepSeek-Claude MCP Server

Enhance Claude's reasoning capabilities with the integration of DeepSeek R1's advanced reasoning engine. This server enables Claude to tackle complex reasoning tasks by leveraging the reasoning capabilites of deepseek r1 model.

🚀 Features

Advanced Reasoning Capabilities

  • Seamlessly integrates DeepSeek R1's reasoning with Claude.

  • Supports intricate multi-step reasoning tasks.

  • Designed for precision and efficiency in generating thoughtful responses.

Related MCP server: DeepSeek-Claude MCP Server

Complete Setup guide

Installing via Smithery

To install DeepSeek-Claude for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @HarshJ23/deepseek-claude-MCP-server --client claude

Prerequisites

  • Python 3.12 or higher

  • uv package manager

  • DeepSeek API key (Sign up at DeepSeek Platform)

  1. Clone the Repository

    git clone https://github.com/harshj23/deepseek-claude-MCP-server.git
cd deepseek-claude-MCP-server

  2. Ensure UV is Set Up

    • Windows: Run the following in PowerShell:

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

    • Mac: Run the following:

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

  3. Create Virtual Environment

    uv venv
source .venv/bin/activate

  4. Install Dependencies

    uv add "mcp[cli]" httpx

  5. Set Up API Key

    Obtain your api key from here : https://platform.deepseek.com/api_keys

  6. Configure MCP Server Edit the claude_desktop_config.json file to include the following configuration: claude_desktop_config.json file

    {
    "mcpServers": {
        "deepseek-claude": {
            "command": "uv",
            "args": [
                "--directory",
                "C:\\ABSOLUTE\\PATH\\TO\\PARENT\\FOLDER\\deepseek-claude",
                "run",
                "server.py"
            ]
        }
    }
}

  7. Run the Server

    uv run server.py

  8. Test Setup

    • Restart Claude Desktop.

    • Verify the tools icon is visible in the interface. tool visible tool verify

    • If the server isn’t visible, consult the troubleshooting guide.

🛠 Usage

Starting the Server

The server automatically starts when used with Claude Desktop. Ensure Claude Desktop is configured to detect the MCP server.

Example Workflow

  1. Claude receives a query requiring advanced reasoning.

  2. The query is forwarded to DeepSeek R1 for processing.

  3. DeepSeek R1 returns structured reasoning wrapped in <ant_thinking> tags.

  4. Claude integrates the reasoning into its final response.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

Available Tools

1 tool
reasonB
Process a query using DeepSeek's R1 reasoning engine and prepare it for integration with 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 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 Claude.
ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

TDQS

B3.3/5.0
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 of behavioral disclosure. It mentions the reasoning engine's capabilities and output formatting with tags, but fails to address critical aspects like rate limits, error handling, authentication needs, or performance characteristics. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.

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 well-structured with clear sections for purpose, technical background, parameters, and returns. It avoids unnecessary fluff, but the second sentence about R1's evolution could be trimmed for brevity without losing clarity. Overall, it's efficient and front-loaded with key information.

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

Completeness3/5

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

Given no annotations, no output schema, and a nested parameter structure, the description is moderately complete. It covers the tool's purpose, parameter details, and return format, but lacks information on error cases, performance, or integration specifics. For a tool with such complexity, it should provide more operational context to be fully adequate.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics by detailing the 'query' parameter's structure with 'context' and 'question' keys, including that 'context' is optional. This goes beyond the bare schema, providing essential context for parameter usage, though it could specify data types or constraints more explicitly.

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 Claude.' It specifies the verb ('process'), resource ('query'), and technology ('DeepSeek R1'), though it doesn't need to differentiate from siblings since none exist. The purpose is specific but could be more precise about what 'process' entails.

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 Claude and the reasoning capabilities, but it lacks explicit guidance on when to use this tool versus alternatives. With no sibling tools, this is less critical, but it doesn't provide context on prerequisites, limitations, or ideal scenarios for application.

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

B3.4/5.0
Disambiguation5/5

With only one tool named 'reason', there is no possibility of confusion or overlap with other tools. The tool has a single, clearly defined purpose: processing queries through DeepSeek's R1 reasoning engine for Claude integration.

Naming Consistency5/5

A single tool inherently demonstrates perfect naming consistency. The tool name 'reason' follows a clear verb-based pattern appropriate for its function, and there are no other tools to create inconsistency.

Tool Count2/5

A single tool server is generally too minimal for most practical applications. While the tool itself performs a specific reasoning task, the server lacks complementary tools for broader reasoning workflows, making it feel incomplete as a standalone server.

Completeness2/5

The server is severely incomplete for a reasoning engine interface. It provides only query processing without any supporting tools for configuration, history management, different reasoning modes, or result validation. This creates significant gaps that will limit agent effectiveness.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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