OpenAI Docs

Provide authoritative, current guidance from OpenAI developer docs using the developers.openai.com MCP server. Always prioritize the developer docs MCP tools over web.run for OpenAI-related questions. Only if the MCP server is installed and returns no meaningful results should you fall back to web search.

Quick start

• Use

  mcp__openaiDeveloperDocs__search_openai_docs

  to find the most relevant doc pages.
• Use

  mcp__openaiDeveloperDocs__fetch_openai_doc

  to pull exact sections and quote/paraphrase accurately.
• Use

  mcp__openaiDeveloperDocs__list_openai_docs

  only when you need to browse or discover pages without a clear query.

OpenAI product snapshots

1. Apps SDK: Build ChatGPT apps by providing a web component UI and an MCP server that exposes your app's tools to ChatGPT.
2. Responses API: A unified endpoint designed for stateful, multimodal, tool-using interactions in agentic workflows.
3. Chat Completions API: Generate a model response from a list of messages comprising a conversation.
4. Codex: OpenAI's coding agent for software development that can write, understand, review, and debug code.
5. gpt-oss: Open-weight OpenAI reasoning models (gpt-oss-120b and gpt-oss-20b) released under the Apache 2.0 license.
6. Realtime API: Build low-latency, multimodal experiences including natural speech-to-speech conversations.
7. Agents SDK: A toolkit for building agentic apps where a model can use tools and context, hand off to other agents, stream partial results, and keep a full trace.

If MCP server is missing

If MCP tools fail or no OpenAI docs resources are available:

1. Run the install command yourself:

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

2. If it fails due to permissions/sandboxing, immediately retry the same command with escalated permissions and include a 1-sentence justification for approval. Do not ask the user to run it yet.
3. Only if the escalated attempt fails, ask the user to run the install command.
4. Ask the user to restart Codex.
5. Re-run the doc search/fetch after restart.

Philosophy

• Prefer root-cause understanding over quick symptom patches.
• Keep guidance evidence-based, explicit, and reproducible.
• Optimize for decisions that reduce rework and operational risk.

Workflow

1. Clarify the product scope (Codex, OpenAI API, or ChatGPT Apps SDK) and the task.
2. Search docs with a precise query.
3. Fetch the best page and the specific section needed (use

   anchor

   when possible).
4. Answer with concise guidance and cite the doc source.
5. Provide code snippets only when the docs support them.

Quality rules

• Treat OpenAI docs as the source of truth; avoid speculation.
• Keep quotes short and within policy limits; prefer paraphrase with citations.
• If multiple pages differ, call out the difference and cite both.
• If docs do not cover the user’s need, say so and offer next steps.

Tooling notes

• Always use MCP doc tools before any web search for OpenAI-related questions.
• If the MCP server is installed but returns no meaningful results, then use web search as a fallback.
• When falling back to web search, restrict to official OpenAI domains (developers.openai.com, platform.openai.com) and cite sources.

Anti-patterns

• Skipping investigation and jumping directly to fixes.
• Making claims without evidence, logs, or reproducible steps.
• Mixing unrelated workstreams in a single execution path.

Constraints / Safety

• Redact secrets, tokens, credentials, and PII by default; never echo raw environment values.
• Prefer safe defaults and avoid irreversible changes without explicit confirmation.

Inputs

• User task context and target environment.
• Relevant constraints, permissions, and preferences required to execute safely.

Outputs

• A concrete next-step response with explicit, reproducible actions.
• A short verification checklist and caveats for the user.

Validation

• Fail fast: stop at the first failed check and do not continue.
• Re-run the required checks before proceeding to the next step.
• Report any failed check and requested follow-up actions clearly.

When to use

• Use this skill when the request matches the skill's intent and scope.
• Do not use it when a different domain or higher-privilege workflow is required.

Constraints / Safety

• Redact secrets, tokens, credentials, and PII by default; never echo raw environment values.
• Prefer safe defaults and avoid irreversible changes without explicit confirmation.

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Philosophy and tradeoffs

• Use this skill when consistent decision-making matters more than one-off execution because project context should drive the approach.
• Principle and mindset: prioritize tradeoffs and constraints over rigid checklists; understand why each step exists.
• Ask this to keep outcomes robust: Why is this the right default, and what could change this outcome?
• How do we adapt if constraints shift?
• What evidence is needed before choosing one path over another?

Anti-patterns and caveats

• Avoid applying this playbook generically without checking repository-specific context.
• NEVER skip required validation gates when behavior changes.
• DO NOT use this skill as a rigid replacement for engineering judgment.
• DON'T ignore warnings or assume one pattern fits all repos.
• Common pitfall: treating anti-patterns as optional.
• Incorrect assumptions here can lead to fragile guidance.
• Warning: wrong sequencing can create avoidable regressions.

Variation and adaptation

• Vary the workflow by team size, risk, and deployment target.
• Use different strategies for small, medium, and large changes.
• Adapt recommendations to the specific environment and avoid repetitive templates.
• Avoid generic or cookie-cutter responses; craft context-specific alternatives.
• Keep outputs diverse and not repetitive.
• Converge on a custom path only after evidence review.
• Different constraints should produce different, non-generic recommendations.

Empowering execution style

• Be capable of exploring multiple options and enabling the team to make safe decisions.
• Unlock confidence by explaining options and tradeoffs clearly.
• Feel free to be creative while staying rigorous and precise.
• Push boundaries with practical alternatives when simple recipes fail.
• Enable outcomes-oriented problem solving.