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OpenAI MCP Server

by thadius83
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Python
Remote

OpenAI MCP 서버

대장간 배지

MCP 프로토콜을 사용하여 Claude에서 직접 OpenAI 모델을 쿼리합니다. 이 포크는 향상된 메시지 처리 기능과 함께 o3-mini 및 gpt-4o-mini 모델 지원을 추가합니다.

클라인 자동 설치

지엑스피1

Related MCP server: SwiftOpenAI MCP Server

특징

  • OpenAI API와 직접 통합

  • 다양한 모델 지원:

    • o3-mini(기본값): 간결한 응답에 최적화됨

    • gpt-4o-mini: 더욱 자세한 응답을 위한 향상된 모델

  • 구성 가능한 메시지 형식

  • 오류 처리 및 로깅

  • MCP 프로토콜을 통한 간단한 인터페이스

설치

Smithery를 통해 설치

Smithery를 통해 Claude Desktop에 OpenAI MCP 서버를 자동으로 설치하는 방법:

npx -y @smithery/cli install @thadius83/mcp-server-openai --client claude

수동 설치

  1. 저장소 복제 :

git clone https://github.com/thadius83/mcp-server-openai.git
cd mcp-server-openai

# Install dependencies
pip install -e .

  1. Claude Desktop 구성 :

이 서버를 기존 MCP 설정 구성에 추가하세요. 참고: 기존 MCP 서버는 구성에 그대로 두고, 이 서버만 함께 추가하세요.

위치:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • 윈도우: %APPDATA%/Claude/claude_desktop_config.json

  • Linux: 기본 MCP 설정 위치를 확인하려면 홈 디렉토리( ~/ )를 확인하세요.

{
  "mcpServers": {
    // ... keep your existing MCP servers here ...
    
    "github.com/thadius83/mcp-server-openai": {
      "command": "python",
      "args": ["-m", "src.mcp_server_openai.server", "--openai-api-key", "your-key-here"],
      "env": {
        "PYTHONPATH": "/path/to/your/mcp-server-openai"
      }
    }
  }
}

  1. OpenAI API 키 받기 :

    • OpenAI 웹사이트를 방문하세요

    • 계정을 생성하거나 로그인하세요

    • API 설정으로 이동

    • 새로운 API 키 생성

    • 위에 표시된 대로 구성 파일에 키를 추가하세요.

  2. 클로드를 다시 시작하세요 :

    • 구성을 업데이트한 후 변경 사항을 적용하려면 Claude를 다시 시작하세요.

용법

이 서버는 OpenAI 모델을 쿼리하는 데 사용할 수 있는 단일 도구인 ask-openai 제공합니다. Claude에서 use_mcp_tool 명령을 사용하여 직접 사용할 수 있습니다.

<use_mcp_tool>
<server_name>github.com/thadius83/mcp-server-openai</server_name>
<tool_name>ask-openai</tool_name>
<arguments>
{
  "query": "What are the key features of Python's asyncio library?",
  "model": "o3-mini"  // Optional, defaults to o3-mini
}
</arguments>
</use_mcp_tool>

모델 비교

  1. o3-mini(기본값)

    • 가장 적합한 용도: 빠르고 간결한 답변

    • 스타일: 직접적이고 효율적

    • 응답 예시:

      Python's asyncio provides non-blocking, collaborative multitasking. Key features:
1. Event Loop – Schedules and runs asynchronous tasks
2. Coroutines – Functions you can pause and resume
3. Tasks – Run coroutines concurrently
4. Futures – Represent future results
5. Non-blocking I/O – Efficient handling of I/O operations

  2. gpt-4o-미니

    • 가장 적합한 용도: 보다 포괄적인 설명

    • 스타일: 상세하고 철저함

    • 응답 예시:

      Python's asyncio library provides a comprehensive framework for asynchronous programming.
It includes an event loop for managing tasks, coroutines for writing non-blocking code,
tasks for concurrent execution, futures for handling future results, and efficient I/O
operations. The library also provides synchronization primitives and high-level APIs
for network programming.

응답 형식

이 도구는 표준화된 형식으로 응답을 반환합니다.

{
  "content": [
    {
      "type": "text",
      "text": "Response from the model..."
    }
  ]
}

문제 해결

  1. 서버를 찾을 수 없습니다 :

    • 구성에서 PYTHONPATH가 올바른 디렉토리를 가리키는지 확인하세요.

    • Python과 pip가 제대로 설치되었는지 확인하세요.

    • 오류를 확인하려면 python -m src.mcp_server_openai.server --openai-api-key your-key-here 직접 실행해보세요.

  2. 인증 오류 :

    • OpenAI API 키가 유효한지 확인하세요

    • args 배열에 키가 올바르게 전달되었는지 확인하세요.

    • 키에 추가 공백이나 문자가 없는지 확인하세요.

  3. 모델 오류 :

    • 지원되는 모델(o3-mini 또는 gpt-4o-mini)을 사용하고 있는지 확인하세요.

    • 귀하의 쿼리가 비어 있지 않은지 확인하세요

    • 토큰 한도를 초과하지 않는지 확인하세요

개발

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest -v test_openai.py -s

원본과의 변경 사항

  • o3-mini 및 gpt-4o-mini 모델에 대한 지원이 추가되었습니다.

  • 개선된 메시지 형식

  • 더 나은 호환성을 위해 온도 매개변수를 제거했습니다.

  • 자세한 사용 예제가 포함된 업데이트된 문서

  • 모델 비교 및 응답 예시 추가

  • 향상된 설치 지침

  • 문제 해결 가이드 추가

특허

MIT 라이센스

Available Tools

1 tool
ask-openaiC

Ask my assistant models a direct question

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesAsk assistant
modelNoo3-mini

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only states the action ('ask a direct question') without disclosing behavioral traits like response format, rate limits, authentication needs, or whether this is a read-only or mutative operation. This leaves significant gaps in understanding how the tool behaves.

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 a single, efficient sentence with no wasted words. It's appropriately sized for a simple tool, though it could be more front-loaded with clearer purpose. The brevity is good, but it borders on under-specification.

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?

Given no annotations, no output schema, and 2 parameters with only 50% schema coverage, the description is incomplete. It doesn't explain return values, error handling, or key behavioral aspects. For a tool that likely interacts with AI models, more context is needed to understand its full scope and limitations.

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?

Schema description coverage is 50% (only 'query' has a description). The description adds no parameter semantics beyond what the schema provides. With 2 parameters and partial schema coverage, the baseline is 3 as the schema does some work, but the description doesn't compensate for the undocumented 'model' parameter.

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

Purpose3/5

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

The description 'Ask my assistant models a direct question' states the action (ask) and target (assistant models), but is vague about what 'assistant models' refers to. It doesn't specify if this is for querying AI models, testing, or something else. Without sibling tools, differentiation isn't needed, but the purpose remains somewhat ambiguous.

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?

No guidance is provided on when to use this tool. The description doesn't mention context, prerequisites, or alternatives. With no sibling tools, this is less critical, but there's still no indication of appropriate use cases or constraints.

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 observedask-openai
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.9/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'ask-openai' has a clear and distinct purpose, making disambiguation perfect.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'ask-openai' follows a verb_noun pattern, but with no other tools to compare, it cannot be inconsistent.

Tool Count2/5

A single tool for an 'OpenAI MCP Server' feels too minimal for the apparent scope, which likely involves interacting with OpenAI's models. This is borderline inadequate, as it may limit functionality and require agents to work around gaps, scoring low due to the mismatch.

Completeness2/5

The tool surface is severely incomplete for an OpenAI server. It only allows asking questions to assistant models, missing obvious operations like listing models, generating text, handling images, or managing conversations, which are core to OpenAI's API capabilities.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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