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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: GPT-MCP Bridge

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_chatA

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

A3.5/5.0
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 of behavioral disclosure. The description mentions the action ('sends messages') and model options, but it doesn't disclose key behavioral traits such as authentication requirements, rate limits, error handling, or what the response looks like (e.g., format, content). 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured with two sentences: the first sets usage context, and the second explains the core action. Every sentence adds value without redundancy, making it efficient and front-loaded for quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (interfacing with an external API) and the absence of annotations and output schema, the description is moderately complete. It covers purpose and usage but lacks details on behavior, error handling, and response format. For a tool with no structured safety or output information, it should provide more context to be fully helpful.

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?

The input schema has 100% description coverage, with clear documentation for both parameters (messages and model). The description adds minimal semantic value beyond the schema—it reiterates the model options but doesn't explain parameter interactions or usage nuances. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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 messages') and resource ('OpenAI's chat completion API'), but since there are no sibling tools, it doesn't need to differentiate from alternatives. It's specific but lacks sibling context, which isn't required here.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to 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 gives clear context for usage. However, it doesn't mention when NOT to use it or discuss alternatives, which is less critical since no sibling tools exist.

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. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.1
    • First observedopenai_chat
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TDQS

A3.5/5.0
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

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'openai_chat' follows a clear verb_noun pattern.

Tool Count2/5

One tool is too few for a server named 'MCP OpenAI Server', which suggests broader OpenAI capabilities. A single chat tool feels thin and under-scoped for the implied domain, lacking operations like embeddings, image generation, or fine-tuning.

Completeness2/5

The tool surface is severely incomplete for an OpenAI server. It only covers chat completions, missing obvious gaps such as embeddings, vision, audio, file operations, and model management, which are core to OpenAI's API offerings.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

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

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