Look up OpenAI API and platform details in official documentation through the documentation MCP server. Retrieve relevant pages, endpoint definitions, and parameters so integration guidance and factual claims are grounded in the fetched source text.
Retrieve authoritative OpenAI API and platform documentation when an integration or claim needs current external evidence.
Review the instructions the agent will receive before you install.
Use the OpenAI Developer Documentation MCP server to search and fetch exact docs (markdown), then base your answer on that text instead of guessing.
If the mcp__openaiDeveloperDocs__* tools are available, use them.
If you are unsure, run codex mcp list and check for openaiDeveloperDocs.
mcp__openaiDeveloperDocs__search_openai_docs → pick the best URL.mcp__openaiDeveloperDocs__fetch_openai_doc → retrieve the exact markdown (optionally with an anchor).mcp__openaiDeveloperDocs__get_openapi_specmcp__openaiDeveloperDocs__list_api_endpointsBase your answer on the fetched text and quote or paraphrase it precisely. Do not invent flags, field names, defaults, or limits.
Provide one of these setup options, then ask the user to restart the Codex session so the tools load:
codex mcp add openaiDeveloperDocs --url https://developers.openai.com/mcp~/.codex/config.toml):
[mcp_servers.openaiDeveloperDocs]
url = "https://developers.openai.com/mcp"
Also point to: https://developers.openai.com/resources/docs-mcp#quickstart
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Check package changes against changesets, the files describing release updates, to assess summaries and required version increases. Use it to review pull requests or uncommitted changes and obtain a structured verdict with errors, warnings, and the required bump.
Run final checks after reviewed SDK changes, covering dependency installation, builds, types, generated declarations, linting, tests, and formatting. The workflow requires every check to pass before the work is marked complete.
Fix a small formatting bug in a credit note shell script while keeping the output label as credit and the amount positive. Run the specified targeted test and summarize the bug, fix, and verification command.
Analyze CSV files with Python and get concise numerical summaries. The skill checks column structure before calculating requested aggregates, includes units when available, and explains assumptions when data is missing or incorrectly formatted.
Compare English SDK documentation with source code to find missing features, incorrect defaults, and outdated explanations. Produce evidence-backed findings or update the requested pages, with verification matched to the scope of the documentation changes.
Analyze existing logs and source files from a completed example run to verify that intended actions actually occurred. Review every entry, classify failures and skips, and report evidence without running examples, controlling processes, or modifying files.