On-chain ZK skill attestations for AI agents on Base — provides MCP tools for attestation, verification, and agent skill registration
Scoring was not performed
Output schemas completely undocumented for all 6 tools. LLMs cannot infer what fields are returned, forcing them to parse unstructured responses or make incorrect downstream tool selections.
Parameter descriptions are minimal (10 - 20 chars), falling well below the 194-char baseline. 'Keccak256 hash' and 'ZK proof data' lack validation constraints, format examples, and guidance on when/why to call each tool.
No error handling guidance. Tools provide no recovery hints if a query fails (e.g., 'agent not found', 'invalid proof format'). LLMs cannot self-correct or retry intelligently.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 16 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 35 | - | v1 |
STDIO transport only. Not remotely accessible and cannot be tested by hosted MCP clients (ChatGPT, Claude). Limits production deployment to local-only contexts.
Parameter validation constraints missing. 'agentId' as 'bigint as string' is not actionable, no guidance on format, length, or example. LLMs will guess and pass invalid values.
No distinction between similar query tools (query_trust_profile vs query_skill_attestations vs list_auditors). Descriptions do not explain when to use each. LLMs conflate them.