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MCP server for auditing AI model compliance against regulatory frameworks (EU AI Act, NIST AI RMF, etc.) and assessing deployment risk
Critical failures across all dimensions. The single tool 'execute_skill_action' has a vague, generic name that violates verb_noun conventions. Description is present but minimal (58 chars, below 10-char baseline). Input schema exists but lacks parameter descriptions entirely, model_manifest_json and target_regulations have no guidance on format, constraints, or usage. No output schema documented. The implementation in mcp_server.py is a stub that prints JSON and exits; client.py contains mock logic unconnected to the MCP protocol. No error handling, no validation, no security considerations. This is not a functional MCP server.
Execute regulatory compliance audit on AI model manifest
Tool name 'execute_skill_action' is generic and violates verb_noun convention. 'execute' and 'skill_action' are vague, LLMs cannot infer what this tool does without reading the description. Should be 'audit_model_compliance' or 'assess_regulatory_risk'.
Input parameters lack descriptions. 'model_manifest_json' and 'target_regulations' have type info but no guidance on expected structure, valid values, or constraints. LLMs cannot determine what fields belong in the manifest or which regulation codes are valid.
No output schema documented. The tool returns a dict with 'audit_passed', 'compliance_score_pct', and 'risk_classification', but this structure is not declared in the tool definition. LLMs cannot plan downstream actions without knowing what fields to expect.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 21 | 2026-07-28+ | v2 |
mcp_server.py is not a functional MCP server. It prints a static JSON message and exits. There is no request/response loop, no tool invocation handler, and no protocol compliance. The actual logic is in client.py, which is disconnected from the MCP transport layer.
No error handling or recovery guidance. If the audit fails or receives invalid input, there is no mechanism to communicate what went wrong or how to retry. Agents have no path forward on failure.