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MCP OpenAI Server

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MCP OpenAI Server

A Model Context Protocol (MCP) server that lets you seamlessly use OpenAI's models right from Claude.

Features

  • Direct integration with OpenAI's chat models

  • Support for multiple models including:

    • gpt-4o

    • gpt-4o-mini

    • o1-preview

    • o1-mini

  • Simple message passing interface

  • Basic error handling

Related MCP server: OpenAI Agents MCP Server

Prerequisites

Installation

First, make sure you've got the Claude Desktop app installed and you've requested an OpenAI API key.

Add this entry to your claude_desktop_config.json (on Mac, you'll find it at ~/Library/Application\ Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "mcp-openai": {
      "command": "npx",
      "args": ["-y", "@mzxrai/mcp-openai@latest"],
      "env": {
        "OPENAI_API_KEY": "your-api-key-here (get one from https://platform.openai.com/api-keys)"
      }
    }
  }
}

This config lets Claude Desktop fire up the OpenAI MCP server whenever you need it.

Usage

Just start chatting with Claude and when you want to use OpenAI's models, ask Claude to use them.

For example, you can say,

Can you ask o1 what it thinks about this problem?

or,

What does gpt-4o think about this?

The server currently supports these models:

  • gpt-4o (default)

  • gpt-4o-mini

  • o1-preview

  • o1-mini

Tools

  1. openai_chat

    • Sends messages to OpenAI's chat completion API

    • Arguments:

      • messages: Array of messages (required)

      • model: Which model to use (optional, defaults to gpt-4o)

Problems

This is alpha software, so may have bugs. If you have an issue, check Claude Desktop's MCP logs:

tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

Development

# Install dependencies
pnpm install

# Build the project
pnpm build

# Watch for changes
pnpm watch

# Run in development mode
pnpm dev

Requirements

  • Node.js >= 18

  • OpenAI API key

Verified Platforms

  • macOS

  • Linux

License

MIT

Author

mzxrai

Available Tools

1 tool
openai_chatB

Use this tool when a user specifically requests to use one of OpenAI's models (gpt-4o, gpt-4o-mini, o1-preview, o1-mini). This tool sends messages to OpenAI's chat completion API using the specified model.

ParametersJSON Schema
NameRequiredDescriptionDefault
messagesYesArray of messages to send to the API
modelNoModel to use for completion (gpt-4o, gpt-4o-mini, o1-preview, o1-mini)gpt-4o

TDQS

B3.1/5.0
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 mentions the action ('sends messages') but omits critical behavioral details like authentication requirements, rate limits, error handling, or response format. For a tool interacting with an external API, this is a significant gap in transparency.

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 concise with two sentences that directly address purpose and usage. It's front-loaded with the usage condition, though it could be slightly more structured. There's minimal waste, earning a high score.

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 the complexity of an API call tool with no annotations and no output schema, the description is incomplete. It lacks details on authentication, error cases, response structure, and operational constraints, which are crucial for effective tool use.

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 100%, so the schema fully documents the parameters. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples or usage tips). This meets the baseline for high schema coverage.

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: 'sends messages to OpenAI's chat completion API using the specified model.' It specifies the verb ('sends'), resource ('messages'), and target ('OpenAI's chat completion API'), though it doesn't need to distinguish from siblings since none exist. The mention of specific models adds precision.

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 provides some usage guidance: 'Use this tool when a user specifically requests to use one of OpenAI's models.' This implies context but lacks explicit when-not-to-use scenarios or alternatives. With no sibling tools, the guidance is adequate but not comprehensive.

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

B3.2/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

The single tool name 'openai_chat' follows a consistent pattern (noun_verb-like structure), and with only one tool, there is no inconsistency to evaluate.

Tool Count2/5

A single tool is too few for a server named 'MCP OpenAI Server', which suggests broader OpenAI functionality. The scope feels thin, as it only covers chat completions, lacking other common operations like embeddings or fine-tuning.

Completeness2/5

The tool surface is severely incomplete for an OpenAI server. It only provides chat completions, missing essential operations such as embeddings, image generation, file handling, or model management, which are core to OpenAI's API.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

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If you are the server author, to access and configure the admin panel.

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