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OpenAI MCP Server

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Python
Remote

OpenAI MCP 服务器

铁匠徽章

使用 MCP 协议直接从 Claude 查询 OpenAI 模型。此分支添加了对 o3-mini 和 gpt-4o-mini 模型的支持，并改进了消息处理。

Cline自动安装

Set up the MCP server from https://github.com/thadius83/mcp-server-openai. 
Use "github.com/thadius83/mcp-server-openai" as the server name in cline_mcp_settings.json.
Analyse the readme and instructions below. Do not create new files in the repo, utilise the findings from pyproject.toml, src/mcp_server_openai/server.py, src/mcp_server_openai/llm.py
Once installed, demonstrate the server's capabilities by using one of its tools.

Installation Steps:
# Clone the repository
git clone https://github.com/thadius83/mcp-server-openai
cd mcp-server-openai

# Install the package
pip install .`

MCP Settings Configuration: The cline_mcp_settings.json should be configured with:
Correct server name format: "github.com/thadius83/mcp-server-openai"
Python module path structure for the server
PYTHONPATH environment variable pointing to the project directory
OpenAI API key passed as a command line argument

Example configuration:
{
  "mcpServers": {
    "github.com/thadius83/mcp-server-openai": {
      "command": "python",
      "args": [
        "-m",
        "src.mcp_server_openai.server",
        "--openai-api-key",
        "your-openai-api-key"
      ],
      "env": {
        "PYTHONPATH": "/path/to/mcp-server-openai"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Requirements:
Python >= 3.10
OpenAI API key
Dependencies installed via pip (mcp>=0.9.1, openai>=1.0.0, click>=8.0.0, pytest-asyncio)

Available Tools:
Tool Name: ask-openai
Description: Ask OpenAI assistant models a direct question
Models Available:
o3-mini (default)
gpt-4o-mini
Input Schema:
{
  "query": "Your question here",
  "model": "o3-mini" // optional, defaults to o3-mini
}

Related MCP server: SwiftOpenAI MCP Server

特征

  • 与 OpenAI 的 API 直接集成

  • 支持多种模型：

    • o3-mini（默认）：针对简洁响应进行了优化

    • GPT-4O-mini：增强模型，可提供更详细的响应

  • 可配置的消息格式

  • 错误处理和日志记录

  • 通过 MCP 协议实现简单的接口

安装

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 OpenAI MCP 服务器：

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

手动安装

  1. 克隆存储库：

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

# Install dependencies
pip install -e .

  1. 配置Claude桌面：

将此服务器添加到您现有的 MCP 设置配置中。注意：请保留配置中现有的 MCP 服务器 - 只需将此服务器添加到它们旁边即可。

地点：

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

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

  • Linux：检查你的主目录（ ~/ ）以获取默认的 MCP 设置位置

{
  "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. 获取 OpenAI API 密钥：

    • 访问OpenAI 的网站

    • 创建账户或登录

    • 导航至 API 设置

    • 生成新的 API 密钥

    • 将密钥添加到您的配置文件中，如上所示

  2. 重启克劳德：

    • 更新配置后，重新启动 Claude 以使更改生效

用法

该服务器提供了一个单独的工具ask-openai ，可用于查询 OpenAI 模型。你可以直接在 Claude 中使用 use_mcp_tool 命令来使用它：

<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>

模型比较

  1. o3-mini（默认）

    • 最适合：快速、简洁的答案

    • 风格：直接、高效

    • 响应示例：

      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-迷你

    • 最适合：更全面的解释

    • 风格：细致、透彻

    • 响应示例：

      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.

响应格式

该工具以标准化格式返回响应：

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

故障排除

  1. 未找到服务器：

    • 验证配置中的 PYTHONPATH 指向正确的目录

    • 确保 Python 和 pip 已正确安装

    • 尝试直接运行python -m src.mcp_server_openai.server --openai-api-key your-key-here来检查错误

  2. 身份验证错误：

    • 检查您的 OpenAI API 密钥是否有效

    • 确保密钥在 args 数组中正确传递

    • 验证密钥中没有多余的空格或字符

  3. 模型误差：

    • 确认您使用的是受支持的型号（o3-mini 或 gpt-4o-mini）

    • 检查您的查询不为空

    • 确保你没有超出令牌限制

发展

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

# Run tests
pytest -v test_openai.py -s

与原版的差异

  • 增加了对 o3-mini 和 gpt-4o-mini 型号的支持

  • 改进的消息格式

  • 删除温度参数以提高兼容性

  • 更新了文档，其中包含详细的使用示例

  • 增加了模型比较和响应示例

  • 增强安装说明

  • 添加了故障排除指南

执照

MIT 许可证

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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