MCP server for OpenAI Agents SDK documentation
Most AI and media services work still happens through a UI a human drives. Openai MCP server moves it into the conversation instead. MCP server for OpenAI Agents SDK documentation.
A Model Context Protocol (MCP) server that provides documentation for the OpenAI Agents SDK by extracting and indexing content from the official documentation website: https://openai.github.io/openai-agents-python/
This project provides both a standalone CLI tool and an MCP server that allows LLMs to access and query the OpenAI Agents SDK documentation intelligently.
The server publishes 1 tool. What each one is for:
Parameters — - query (string, required) - Feature name or natural language questionInstallation goes through your MCP client rather than a global install: point it at openai-agents-sdk-mcp on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Configuration is passed through the environment: OPENAI_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Openai's toolset — Parameters — is a fair guide to whether it matches your workflow.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Parameters | - query (string, required) - Feature name or natural language question |
{
"mcpServers": {
"openai-agents-sdk-docs": {
"command": "openai-agents-sdk-mcp",
"env": {
"OPENAI_API_KEY": "sk-your-api-key-here"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.