MCP Tool Evaluation with Claude Code: Logs and Review Trail

A production playbook for MCP tool evaluation in cross-industry operations using Claude Code: logs and review trail, run-scoped inputs, logs, typed results, and artifacts.

Audience: AI platform teams adopting MCP

The problem

AI platform teams adopting MCP need MCP tool evaluation to run repeatedly against tool definitions, auth policy, traces, and test cases. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.

Implementation path

Capture the MCP tool evaluation run as product telemetry: input manifest, tool calls, model output, result validation, artifact upload, and terminal status.

Tradeoffs and failure modes

More observability means more storage and retention policy, but support stops depending on screenshots of agent chats. For MCP tool evaluation, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.

Review checklist

- input manifest captured
- tool calls retained
- terminal status recorded
- result JSON validated
- artifacts linked
- exceptions separated from final answer

Run this on Argo