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thadius83

OpenAI MCP Server

by thadius83

Servidor MCP de OpenAI

insignia de herrería

Consulta modelos de OpenAI directamente desde Claude mediante el protocolo MCP. Esta bifurcación añade compatibilidad con los modelos o3-mini y gpt-4o-mini con un manejo de mensajes mejorado.

Instalación automática de Cline

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

Características

  • Integración directa con la API de OpenAI

  • Soporte para múltiples modelos:

    • o3-mini (predeterminado): optimizado para respuestas concisas

    • gpt-4o-mini: Modelo mejorado para respuestas más detalladas

  • Formato de mensaje configurable

  • Manejo y registro de errores

  • Interfaz sencilla a través del protocolo MCP

Instalación

Instalación mediante herrería

Para instalar OpenAI MCP Server para Claude Desktop automáticamente a través de Smithery :

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

Instalación manual

  1. Clonar el repositorio :

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

# Install dependencies
pip install -e .
  1. Configurar Claude Desktop :

Agregue este servidor a su configuración de MCP existente. Nota: Conserve todos los servidores MCP existentes en la configuración; simplemente agregue este junto con ellos.

Ubicación:

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

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

  • Linux: Verifique su directorio de inicio ( ~/ ) para conocer la ubicación de la configuración predeterminada de MCP

{
  "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. Obtenga una clave API de OpenAI :

    • Visita el sitio web de OpenAI

    • Crea una cuenta o inicia sesión

    • Navegar a la configuración de la API

    • Generar una nueva clave API

    • Agregue la clave a su archivo de configuración como se muestra arriba

  2. Reiniciar Claude :

    • Después de actualizar la configuración, reinicie Claude para que los cambios surtan efecto.

Uso

El servidor proporciona una única herramienta ask-openai , que permite consultar modelos OpenAI. Puede usarla directamente en Claude con el comando use_mcp_tool:

<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>

Comparación de modelos

  1. o3-mini (predeterminado)

    • Ideal para: respuestas rápidas y concisas

    • Estilo: Directo y eficiente

    • Ejemplo de respuesta:

      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-mini

    • Ideal para: explicaciones más completas

    • Estilo: Detallado y minucioso.

    • Ejemplo de respuesta:

      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.

Formato de respuesta

La herramienta devuelve respuestas en un formato estandarizado:

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

Solución de problemas

  1. Servidor no encontrado :

    • Verifique que PYTHONPATH en su configuración apunte al directorio correcto

    • Asegúrese de que Python y pip estén instalados correctamente

    • Intente ejecutar python -m src.mcp_server_openai.server --openai-api-key your-key-here directamente para verificar si hay errores

  2. Errores de autenticación :

    • Comprueba que tu clave API de OpenAI sea válida

    • Asegúrese de que la clave se pase correctamente en la matriz de argumentos

    • Verifique que no haya espacios ni caracteres adicionales en la clave

  3. Errores del modelo :

    • Confirme que está utilizando modelos compatibles (o3-mini o gpt-4o-mini)

    • Comprueba que tu consulta no esté vacía

    • Asegúrese de no exceder los límites de tokens

Desarrollo

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

# Run tests
pytest -v test_openai.py -s

Cambios respecto al original

  • Se agregó soporte para los modelos o3-mini y gpt-4o-mini

  • Formato de mensajes mejorado

  • Se eliminó el parámetro de temperatura para una mejor compatibilidad.

  • Documentación actualizada con ejemplos de uso detallados

  • Se agregaron ejemplos de comparación y respuesta de modelos

  • Instrucciones de instalación mejoradas

  • Se agregó una guía de solución de problemas

Licencia

Licencia MIT

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

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

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