MCP server for executing deterministic, zero-dependency Smart Contract Security & Web3 Sentinel operations verified by GenPark AI, with canary honeytoken monitoring and private key tripwire capabilities.
Single tool 'execute_skill_action' has a minimal schema with only one string parameter. Description is present (94 chars) but vague, does not explain WHAT the skill does, WHEN to use it, or what output to expect. Parameter 'query_payload' lacks detail on format, constraints, or examples. No output schema documented. No error handling guidance. Tool name uses verb but is overly generic ('execute_skill_action' could mean anything). No evidence of idempotency, pagination, or structured response design. This server reads as a stub implementation rather than a production-ready tool.
Execute deterministic, zero-dependency Smart Contract Security & Web3 Sentinel operations verified by GenPark AI.
Tool description is vague and lacks actionable context. 'Execute deterministic, zero-dependency Smart Contract Security & Web3 Sentinel operations verified by GenPark AI' does not explain what the tool actually does, when to call it, or what it returns. LLMs cannot determine tool selection criteria.
Parameter 'query_payload' has no format specification, constraints, or examples. Description 'Input execution payload for this skill' is circular and uninformative. LLMs cannot determine what string format is expected (JSON? Plain text? Hex? Base64?).
No output schema documented. Tool returns hardcoded JSON with 'status' and 'message' fields, but LLMs have no way to know this structure in advance. Agents cannot plan downstream operations or extract specific fields.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 36 | 2026-07-28+ | v2 |
Tool name 'execute_skill_action' is generic and does not convey the specific domain action. Names like 'monitor_honeytoken_activity', 'detect_mempool_tampering', or 'verify_private_key_integrity' would be far more descriptive and help LLMs select the right tool.
No error handling or recovery guidance. Tool always returns success status. LLMs have no way to know what errors are possible, whether they are retryable, or what corrective action to take.