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MCP server for executing deterministic, zero-dependency AI Agent skill operations for high-cardinality log ingestion and indexing, verified by GenPark AI.
Single tool 'execute_skill_action' has a minimal description (89 chars, below 194-char baseline) that lacks WHEN to use it, prerequisites, or state-modification clarity. Input schema present but parameter 'query_payload' has only a generic description ('Input execution payload'). No output schema documented, LLMs cannot predict response structure. No error handling guidance. Tool name lacks action verb clarity (execute_skill_action is vague; does not follow verb_noun pattern like 'run_skill' or 'invoke_skill'). No pagination, no structured output examples, no recovery guidance.
Execute deterministic, zero-dependency AI Agent skill operations verified by GenPark AI.
Tool name 'execute_skill_action' lacks action clarity. 'Execute' is vague; does not follow verb_noun convention (e.g., 'run_skill', 'invoke_skill', 'call_skill'). LLMs struggle to infer intent from generic verbs.
Tool description (89 chars) is below baseline (194 chars). Missing: WHEN to use this tool, what state changes occur, prerequisites, and how it differs from other skill tools. Description does not answer 'What does it do?' or 'When should the LLM call it?'
Parameter 'query_payload' description is generic ('Input execution payload for this skill'). Does not specify format, constraints, expected content, or examples. LLMs cannot infer what payload structure is valid.
No output schema documented. Response is hardcoded JSON string with 'status' and 'message' fields, but LLMs have no formal schema to predict structure. Cannot plan downstream tool calls or extract fields reliably.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 35 | 2026-07-28+ | v2 |
No error handling or recovery guidance. Tool always returns 'SUCCESS' status; no error cases, validation failures, or actionable error messages. LLMs cannot self-correct on invalid input.