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DeepSeek MCP Server

by moyu6027
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Python
Remote

🧠 DeepSeek MCP 서버

DeepSeek MCP 서버

🚀 특징

DeepSeek R1의 고급 추론 엔진을 통합하여 Claude의 추론 능력을 향상시키세요 . 이 서버를 통해 Claude는 deepseek r1 모델의 추론 기능을 활용하여 복잡한 추론 작업을 처리할 수 있습니다.

  • DeepSeek R1(The Brain)은 고급 추론 플래너 역할을 합니다.

    • 다단계 논리 분석 전략 계획

    • 구조 인지 프레임워크

    • 자신감과 불확실성을 평가합니다

    • 추론 품질을 모니터링합니다

    • 에지 케이스와 바이어스를 감지합니다.

  • 클로드(집행자)는 추론 계획을 실행합니다.

    • 구조화된 분석을 실행합니다

    • 계획된 전략을 구현합니다

    • 최종 응답을 전달합니다

    • 사용자 상호작용을 처리합니다

    • 시스템 통합을 관리합니다

Related MCP server: Deepseek R1 MCP Server

🚀 특징

고급 추론 능력

  • 복잡한 다단계 추론 작업을 지원합니다.

  • 신중한 응답을 생성하기 위한 정확성과 효율성을 위해 설계되었습니다.

  • 무제한 개방형 API 사용

전체 설정 가이드

필수 조건

  • Python 3.12 이상

  • uv 패키지 관리자

  • INFINI_API_KEY DeepSeek용(无问芯穹에서 가입)

  1. 저장소 복제

    지엑스피1

  2. UV가 설정되어 있는지 확인하세요

    • Windows : PowerShell에서 다음을 실행합니다.

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

    • Mac : 다음을 실행하세요.

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

  3. 가상 환경 만들기

    uv venv
source .venv/bin/activate

  4. 종속성 설치

    uv add "mcp[cli]" httpx

  5. API 키 설정

    echo "INFINI_API_KEY=your_key_here" > .env

  6. 서버 설치

    mcp install server.py -f .env

  7. MCP 서버 구성 claude_desktop_config.json 파일을 편집하여 다음 구성을 포함합니다.

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

  8. 서버 실행

    uv run server.py

🛠 사용법

서버 시작

Claude Desktop과 함께 사용하면 서버가 자동으로 시작됩니다. Claude Desktop이 MCP 서버를 감지하도록 구성되어 있는지 확인하세요.

워크플로 예시

  1. 클로드는 고급 추론이 필요한 질문을 받습니다.

  2. 해당 쿼리는 처리를 위해 DeepSeek R1로 전달됩니다.

  3. DeepSeek R1은 <ant_thinking> 태그로 묶인 구조화된 추론을 반환합니다.

  4. 클로드는 추론을 최종 반응으로 통합합니다.

📄 라이센스

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 라이선스 파일을 참조하세요.

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

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