MCP server for executing deterministic, zero-dependency AI Agent skill operations for relationship-based access control (ReBAC) authorization policy evaluation, verified by GenPark AI.
Single tool with minimal definition quality. Tool name 'execute_skill_action' is generic and does not follow verb_noun pattern clearly (execute_skill_action vs. evaluate_rebac_permission would be more specific). Description is 108 chars, adequate length, but lacks critical context: does not state WHAT the tool does operationally, WHEN to use it, or what the output structure contains. Input schema present but severely underdeveloped: single string parameter 'query_payload' with minimal description ('Input execution payload for this skill') provides no guidance on format, expected content, or constraints. No output schema documented, LLM cannot predict response structure. No error handling guidance. Tool appears to be a pass-through wrapper with no real validation or structured behavior.
Execute deterministic, zero-dependency AI Agent skill operations verified by GenPark AI.
Generic tool name 'execute_skill_action' does not convey specific action. Should be 'evaluate_rebac_permission' or similar to match actual domain (ReBAC authorization evaluation).
Input parameter 'query_payload' is a free-form string with no schema, format constraints, or examples. LLM cannot determine what to pass. Should define structured input with fields like subject_entity, relation, object_resource (as shown in client.py).
No output schema documented. Response structure is hardcoded JSON string in mcp_server.py but not declared in tool definition. LLM cannot plan downstream operations or extract fields like rebac_evaluation_id, permission_allowed, derived_role_chain.
Tool description lacks operational clarity. Does not explain: What ReBAC evaluation means, when to call this vs. other authorization tools, what the output fields represent, or how to interpret permission_allowed result.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 36 | 2026-07-28+ | v2 |
No error handling or recovery guidance. If evaluation fails or subject/relation/object are invalid, LLM receives no actionable error message or next steps.