Data Extraction Quality Audit with Claude Code: MCP Tool Boundary
A production playbook for data extraction quality audit in cross-industry operations using Claude Code: mcp tool boundary, run-scoped inputs, logs, typed results, and artifacts.
Audience: AI operations teams
The problem
AI operations teams need data extraction quality audit to run repeatedly against extracted fields, source docs, validation failures, and review notes. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.
Implementation path
Expose only the MCP tools needed for data extraction quality audit, validate tool arguments, keep credentials in the owning service, and log each call outside the sandbox.
Tradeoffs and failure modes
Narrow tool boundaries reduce agent flexibility, but make the integration reviewable and supportable. For data extraction quality audit, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.
Tool policy
tool: data-extraction-quality-audit_lookup
agent: Claude Code
input_scope: /skill/.argo/inputs
credential_owner: broker
log_arguments: true
network_policy: allowlisted
Run this on Argo