MCP OpenAI Server
Provides access to OpenAI's chat completion API, enabling use of models like gpt-4o, gpt-4o-mini, o1-preview, and o1-mini for message-based interactions.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP OpenAI Serverask gpt-4o to explain quantum entanglement in simple terms"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Node.js >= 18 (includes
npmandnpx)
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
openai_chatSends 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*.logDevelopment
# Install dependencies
pnpm install
# Build the project
pnpm build
# Watch for changes
pnpm watch
# Run in development mode
pnpm devRequirements
Node.js >= 18
OpenAI API key
Verified Platforms
macOS
Linux
License
MIT
Author
Available Tools
1 toolopenai_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.
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | Array of messages to send to the API | |
| model | No | Model to use for completion (gpt-4o, gpt-4o-mini, o1-preview, o1-mini) | gpt-4o |
TDQS
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.
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.
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.
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.
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.
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.1- First observed
openai_chat
TDQS
Scored across 1 tool
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.
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.
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.
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
Related MCP Connectors
- QuallaaOAuthcom.quallaa
Talk to your public-facing AI from any MCP client — Claude, ChatGPT, Cursor, Cline, Windsurf.
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Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
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