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API deployment checklist | OpenAI API Sora 2 Prompting Guide Codex Prompting Guide Gpt-image-1.5 Prompting Guide GPT-5.2 Prompting Guide Transcribing User Audio with a Separate Realtime Request Modernizing your Codebase with Codex GitHub - openai/openai-sora-sample-app: Sample app to get started using the Video API with Sora GitHub - openai/openai-apps-sdk-examples: Example apps for the Apps SDK GitHub - openai/openai-chatkit-advanced-samples: Starter app to build with OpenAI ChatKit SDK GitHub - openai/openai-chatkit-starter-app: Starter app to build with OpenAI ChatKit + Agent Builder Rate limits | OpenAI API Web search | OpenAI API Getting started with datasets | OpenAI API Prompt optimizer | OpenAI API Verifying gpt-oss implementations How to run gpt-oss locally with LM Studio Fine-tuning with gpt-oss and Hugging Face Transformers How to run gpt-oss locally with Ollama Function calling | OpenAI API Models | OpenAI API Reasoning best practices | OpenAI API Reasoning models | OpenAI API Background mode | OpenAI API Batch API | OpenAI API Conversation state | OpenAI API File search | OpenAI API Flex processing | OpenAI API MCP and Connectors | OpenAI API Code Interpreter | OpenAI API Quickstart - OpenAI Agents SDK Build Hour: Agentic Tool Calling Build Hour: Built-In Tools Reasoning best practices | OpenAI API Graders | OpenAI API Evaluation best practices | OpenAI API Working with evals | OpenAI API Guardrails - OpenAI Agents SDK Latency optimization | OpenAI API Optimizing LLM Accuracy | OpenAI API Agent orchestration - OpenAI Agents SDK Production best practices | OpenAI API Realtime transcription | OpenAI API Optimizing LLM Accuracy | OpenAI API Realtime and audio | OpenAI API Realtime conversations | OpenAI API Responses guide Migrate to the Responses API | OpenAI API Speech to text | OpenAI API Supervised fine-tuning | OpenAI API Tracing - OpenAI Agents SDK Vision fine-tuning | OpenAI API Audio and speech | OpenAI API GitHub - openai/openai-cs-agents-demo: Demo of a customer service use case implemented with the OpenAI Agents SDK Voice agents | OpenAI API Fine-tuning best practices | OpenAI API GitHub - openai/openai-agents-python: A lightweight, powerful framework for multi-agent workflows GitHub - openai/openai-agents-js: A lightweight, powerful framework for multi-agent workflows and voice agents Agents SDK | OpenAI API Using tools | OpenAI API Computer use | OpenAI API GitHub - openai/openai-cua-sample-app: Learn how to use CUA (our Computer Using Agent) via the API on multiple computer environments. GitHub - openai/openai-testing-agent-demo: Demo of a UI testing agent using the OpenAI CUA model and the Responses API. Model optimization | OpenAI API GitHub - openai/openai-fm: Code for openai.fm, a demo for the OpenAI Speech API Predicted Outputs | OpenAI API GitHub - openai/openai-realtime-console: React app for inspecting, building and debugging with the Realtime API Building Voice Agents GitHub - openai/openai-realtime-solar-system: Demo showing how to use the OpenAI Realtime API to navigate a 3D scene via tool calling GitHub - openai/openai-realtime-twilio-demo Reinforcement fine-tuning | OpenAI API GitHub - openai/openai-responses-starter-app: Starter app to build with the OpenAI Responses API Structured model outputs | OpenAI API GitHub - openai/openai-structured-outputs-samples: Sample apps to help developers get started with Structured Outputs Voice agents | OpenAI API Model optimization | OpenAI API GitHub - openai/openai-realtime-agents: This is a simple demonstration of more advanced, agentic patterns built on top of the Realtime API. GitHub - openai/openai-support-agent-demo: Demo of a customer support agent interface using NextJS and the OpenAI Responses API with File Search Building Voice Agents Generate images with high input fidelity AI app development: Concept to production Model optimization Building agents Eval Driven System Design - From Prototype to Production Multi-Agent Portfolio Collaboration with OpenAI Agents SDK o3/o4-mini Function Calling Guide Exploring Model Graders for Reinforcement Fine-Tuning Guide to Using the Responses API Reinforcement Fine-Tuning for Conversational Reasoning with the OpenAI API Evals API Use-case - Responses Evaluation Comparing Speech-to-Text Methods with the OpenAI API Generate images with GPT Image Multi-Tool Orchestration with RAG approach using OpenAI Multi-Language One-Way Translation with the Realtime API Doing RAG on PDFs using File Search in the Responses API How to use the Usage API and Cost API to monitor your OpenAI usage Leveraging model distillation to fine-tune a model Orchestrating Agents: Routines and Handoffs Prompt Caching 101 Developing Hallucination Guardrails
Docs MCP | OpenAI Developers
2026-01-06 · via OpenAI Developers

OpenAI hosts a public Model Context Protocol (MCP) server for developer documentation on developers.openai.com and platform.openai.com.

Server URL (streamable HTTP): https://developers.openai.com/mcp

What it provides

  • Read-only access to OpenAI developer documentation (search + page content).
  • A way to pull documentation into your agent’s context while you work.

This MCP server is documentation-only. It does not call the OpenAI API on your behalf.

Quickstart

You can connect Codex to MCP servers in the CLI or IDE extension. The configuration is shared between both so you only have to set it up once.

Add the server using the Codex CLI:

codex mcp add openaiDeveloperDocs --url https://developers.openai.com/mcp

Verify it’s configured:

codex mcp list

Alternatively, you can add it in ~/.codex/config.toml directly:

[mcp_servers.openaiDeveloperDocs]
url = "https://developers.openai.com/mcp"

To have Codex reliably use the MCP server, add this snippet to your AGENTS.md:

Always use the OpenAI developer documentation MCP server if you need to work with the OpenAI API, ChatGPT Apps SDK, Codex,… without me having to explicitly ask.

Tips

  • If you don’t have the snippet in the AGENTS.md file, you need to explicitly tell your agent to consult the Docs MCP server for the answer.
  • If you have more than one MCP server, keep server names short and descriptive to aid the agent in selecting the server.

OpenAI Docs Skill

If you use skills in your AI tooling, pair this MCP server with the OpenAI Docs Skill. It tells the agent to use Docs MCP tools first for OpenAI questions, then fall back to official OpenAI domains.

  1. Install the skill from the OpenAI skills repository.
  2. Confirm you configured this Docs MCP server at https://developers.openai.com/mcp.
  3. Enable the skill for your project or session in your agent tooling.
  4. Ask OpenAI product/API questions and request citations so answers stay traceable to docs sources.