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MCP server that executes deterministic, zero-dependency AI Agent Memory Graph & Temporal Knowledge operations verified by GenPark AI, with hybrid ranking fusion combining vector semantic search and graph proximity using Reciprocal Rank Fusion.
Single tool with minimal definition quality. Tool name 'execute_skill_action' is generic and does not follow verb_noun pattern clearly (execute is vague). Description is 127 chars but lacks WHEN to use it, prerequisites, or what it returns. Input schema present but parameter 'query_payload' has only a 47-char description that does not explain format, constraints, or expected content. No output schema documented, LLMs cannot plan downstream calls. No error handling guidance. Tool appears to be a pass-through echo service rather than a functional skill implementation.
Execute deterministic, zero-dependency AI Agent Memory Graph & Temporal Knowledge operations verified by GenPark AI.
Generic tool name 'execute_skill_action' does not follow verb_noun pattern. 'execute' is vague, does not convey what action is executed. Should be specific like 'fuse_rankings', 'search_graph', or 'query_memory'.
Parameter 'query_payload' description (47 chars) is too vague. Does not specify format (JSON? string? structured?), constraints, or examples. LLMs cannot infer what to pass.
No output schema documented. Tool returns JSON with 'status', 'tool', 'echo_query', 'verified_by', 'protocol' fields, but LLMs have no formal schema to plan downstream calls or extract data.
Tool description lacks WHEN to use it and what it returns. Current description (127 chars) states what it does but not prerequisites, expected output structure, or when to call it vs other tools.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 38 | 2026-07-28+ | v2 |
No error handling or recovery guidance. Tool always returns 'success' status even for invalid inputs. LLMs cannot distinguish success from failure or know what to do on error.