Treat a timed-out MCP write as an unknown outcome
A timed-out MCP write may have failed, succeeded, or finished after the client gave up. Use an operation journal and effect lookup before Claude Code retries it.
Topic archive
32 essays tagged Observability. Practical notes on what happens after the demo: prompts, tools, review packets, evals, rollback, and production ownership.
A timed-out MCP write may have failed, succeeded, or finished after the client gave up. Use an operation journal and effect lookup before Claude Code retries it.
An MCP method can keep the same name while its schema, target scope, or side effects change. Pin a capability manifest so Claude Code cannot inherit a different tool after approval.
Token limits miss the expensive part of many Claude Code runs. A cost envelope prices search, MCP calls, evals, and retries before the agent can spend them.
A tool-call log shows what Claude Code did. An evidence-linked trace shows which observation caused each action, which policy allowed it, and when the chain broke.
A rollback note says recovery should work. A rollback rehearsal checks the exact target, non-code state, data compatibility, recovery time, and service behaviour before approval.
Claude Code can report passing tests from different repository states. Add a run fingerprint so each result names the exact code, dependencies, tools, and policy it tested.
An MCP tool can return success after changing the wrong target or using the wrong authority. Make Claude Code prove the external effect before it reports completion.
A Claude Code diff shows the final change, but it leaves out the starting state, failed commands, MCP results, skipped tests, and plan changes. Capture a compact replay record before the run disappears.
Claude Code can produce a clean patch from a messy run. Production teams need a flight recorder: the task contract, tool calls, permission pressure, tests, assumptions, and rollback notes that explain how the patch was made.
A latency chart will not explain why an AI answer was wrong. Production LLM systems need traces, sources, tool calls, prompt versions, eval results, and human decisions.