What this error means
openai.RateLimitError: Error code: 429 - Request too large for gpt-4o in organization on tokens per min (TPM): Limit 30000, Requested 68490 is a OpenAI API failure pattern reported for developers trying to understand how mcp server tool outputs bloat context windows and trigger api rate limits; find workarounds to trim response fields before sending to llm. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
Source: https://github.com/github/github-mcp-server/issues/142 — When list_commits() returns 30 results, each at 5-6KB, total context exceeds 64K tokens and triggers TPM 429 errors on OpenAI. Tier-1 API user affected. Issue actively discussed, recently closed May 21 2026 after minimal response trimming was added. Strong commercial value: affects paid API users + MCP ecosystem. Category = OpenAI API (root cause is rate limit triggered by tool output size).
Common causes
- Source: https://github.com/github/github-mcp-server/issues/142 — When list_commits() returns 30 results, each at 5-6KB, total context exceeds 64K tokens and triggers TPM 429 errors on OpenAI. Tier-1 API user affected. Issue actively discussed, recently closed May 21 2026 after minimal response trimming was added. Strong commercial value: affects paid API users + MCP ecosystem. Category = OpenAI API (root cause is rate limit triggered by tool output size).
Quick fixes
- Confirm the exact error signature matches
openai.RateLimitError: Error code: 429 - Request too large for gpt-4o in organization on tokens per min (TPM): Limit 30000, Requested 68490. - Check the OpenAI API account, local tool state, and provider configuration involved in the failing workflow.
- Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.
Platform/tool-specific checks
- Verify the command, editor, extension, or API client that produced the error.
- Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
- Avoid deleting credentials, local model data, or project settings until the failing scope is clear.
Step-by-step troubleshooting
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- Keep the working fix documented for the team or deployment environment.
How to prevent it
- Keep provider/tool configuration documented.
- Record non-secret diagnostics such as tool version, provider name, model name, and command path.
- Add a lightweight check before CI or production workflows depend on the tool.