Debugging ChatGPT plugins beyond the screenshot

Reliable troubleshooting of failed MCP plugin updates requires evidence that connects the customer’s request, the selected tool call, backend processing, and the source system’s actual record state, rather than relying on an assistant’s success message alone. In the example, an asynchronous renewal-date update was accepted but later failed due to a CRM version conflict, while ChatGPT incorrectly presented acceptance as completion, leaving the CRM unchanged. Developers should retain sanitized, access-controlled diagnostic records containing correlation IDs, tool and schema versions, timestamps, intended operation, job states, downstream errors, and record-verification results, while keeping internal metadata, credentials, and full conversations out of model-facing responses and support tickets. OpenTelemetry and trace-context standards can help link services, though application-specific account authorization and backend correlation remain necessary. Reproduction should use sanitized inputs and test environments to verify that pending, failed, and completed states are accurately conveyed, with regression tests covering tool selection, arguments, errors, confirmation language, and follow-up status checks. Before retrying an uncertain write, support should inspect the source record and confirm the original operation is no longer in progress, then use application-level idempotency keys or version-based conditions to avoid duplicate changes, since MCP request IDs do not provide write safety.

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