AI governance MCP: EU AI Act, GDPR and ISO/IEC 42001 checks, evidence and audit reports.
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Scored across 22 tools
Most names suggest distinct actions, but several pairs overlap in purpose without descriptions to clarify boundaries: assess_ai_risks vs assess_ai_system, review_changes vs review_codebase vs review_agent_action, and map_requirements vs explain_requirement. An agent could reasonably struggle to choose the correct tool in these clusters.
The set largely follows a consistent snake_case verb_noun pattern across assess_, check_, generate_, get_, review_, map_, plan_, and register_ prefixes. Minor deviations like health_check and get_about are readable but slightly break the pattern.
At 22 tools, the surface is heavy for a governance server and sits at the borderline where each tool's unique value becomes harder to justify without descriptions. It is not excessive for a broad governance platform, but it is on the high side.
The tools cover a broad governance lifecycle: framework discovery, requirement mapping, risk assessment, control checks, policy and evidence generation, audit reporting, agent review, and watch registration. Some lifecycle operations could be missing, but the surface appears mostly complete for the stated governance purpose.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
assess_ai_risksassess_ai_systemassess_promptcheck_agent_configcheck_controlsdiscover_frameworksexplain_requirementgenerate_agent_policygenerate_audit_reportgenerate_evidencegenerate_testsget_aboutget_audit_reportget_example_promptsget_watch_reporthealth_checkmap_requirementsplan_next_stepsregister_watchreview_agent_actionreview_changesreview_codebase
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