MCP server for executing deterministic, zero-dependency Influencer Marketing skill operations verified by GenPark AI, specifically for matching UGC creators to brief auctions.
Single tool 'execute_skill_action' has critical definition gaps. Description is present (89 chars) but vague, does not explain WHAT skill operations are supported, WHEN to use it, or what the response structure contains. Input schema exists but is severely underspecified: 'query_payload' is a bare string with no format, length, or content constraints. No output schema documented. Tool name uses generic verb 'execute' rather than domain-specific action (e.g., 'match_ugc_creators', 'auction_brief'). No error handling guidance. No parameter validation rules. Fails 6 of 7 critical naming/description/schema checks.
Execute deterministic, zero-dependency Influencer Marketing skill operations verified by GenPark AI.
Tool name 'execute_skill_action' is generic and does not reflect the actual domain operation (UGC creator matching, brief auctioning). LLMs cannot infer intent from the name alone. Should be 'match_ugc_creators_for_brief' or similar verb_noun pattern.
Input parameter 'query_payload' is a free-form string with no schema constraints, format specification, or examples. LLMs cannot determine what structure or content is expected. Should define enum values, JSON structure, or at minimum length/pattern constraints.
No output schema documented. Tool returns JSON text but LLMs do not know what fields to expect (status, message, brief_auction_id, creators_matched_count, etc.). Prevents downstream tool chaining and forces LLM to parse unstructured text.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 31 | 2026-07-28+ | v2 |
Tool description (89 chars) is too vague: 'Execute deterministic, zero-dependency Influencer Marketing skill operations verified by GenPark AI.' Does not state WHAT operations are supported, WHEN to call it, or what it returns. Baseline for A+ tools is 50-200 chars with explicit WHAT/WHEN/RETURN guidance.
No error handling or recovery guidance. Tool always returns 'SUCCESS' in mock implementation. Real errors (invalid payload, service unavailable, no matching creators) have no documented error responses or LLM-actionable guidance.