About Chatgpt
Chatgpt is a stdio MCP server that sends text to OpenAI's GPT-4o for summarization, analysis, comparison, and reasoning. It is intended for LangGraph-based assistants that need an external LLM tool.
Key features
- 01Send text to GPT-4o
- 02Summarize long documents
- 03Analyze configuration files
- 04Compare options and reason over text
Use cases
- 01Summarize a long document
- 02Analyze a configuration file
- 03Compare alternatives
- 04Add external LLM reasoning to a LangGraph assistant
MCP support
Exposes a single `ask_chatgpt` tool that forwards provided content to GPT-4o. It communicates over MCP stdio.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README🧠 Ask ChatGPT - MCP Server (Stdio)
This is a Model Context Protocol (MCP) stdio server that forwards prompts to OpenAI’s ChatGPT (GPT-4o). It is designed to run inside LangGraph-based assistants and enables advanced summarization, analysis, and reasoning by accessing an external LLM.
📌 What It Does
This server exposes a single tool:
{
"name": "ask_chatgpt",
"description": "Sends the provided text ('content') to an external ChatGPT (gpt-4o) model for advanced reasoning or summarization.",
"parameters": {
"type": "object",
"properties": {
"content": {
"type": "string",
"description": "The text to analyze, summarize, compare, or reason about."
}
},
"required": ["content"]
}
}
Use this when your assistant needs to:
Summarize long documents
Analyze configuration files
Compare options
Perform advanced natural language reasoning
🐳 Docker Usage
Build and run the container:
docker build -t ask-chatgpt-mcp .
docker run -e OPENAI_API_KEY=your-openai-key -i ask-chatgpt-mcp
🧪 Manual Test
Test the server locally using a one-shot request:
echo '{"method":"tools/call","params":{"name":"ask_chatgpt","arguments":{"content":"Summarize this config..."}}}' | \
OPENAI_API_KEY=your-openai-key python3 server.py --oneshot
🧩 LangGraph Integration
To connect this MCP server to your LangGraph pipeline, configure it like this:
("chatgpt-mcp", ["python3", "server.py", "--oneshot"], "tools/discover", "tools/call")
⚙️ MCP Server Config Example
Here’s how to configure the server using an mcpServers JSON config:
{
"mcpServers": {
"chatgpt": {
"command": "python3",
"args": [
"server.py",
"--oneshot"
],
"env": {
"OPENAI_API_KEY": "<YOUR_OPENAI_API_KEY>"
}
}
}
}
🔍 Explanation
"command": Runs the script with Python
"args": Enables one-shot stdin/stdout mode
"env": Injects your OpenAI key securely
🌍 Environment Setup
Create a .env file (auto-loaded with python-dotenv) or export the key manually:
OPENAI_API_KEY=your-openai-key
Or:
export OPENAI_API_KEY=your-openai-key
📦 Dependencies
Installed during the Docker build:
openai
requests
python-dotenv
📁 Project Structure
.
├── Dockerfile # Docker build for the MCP server
├── server.py # Main stdio server implementation
└── README.md # You're reading it!
🔐 Security Notes
Never commit .env files or API keys.
Store secrets in secure environment variables or secret managers.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.