Loading ToolBench
AI tool discovery engine with live search agent, MCP server, and open-source dataset. Provides tool hunting, consultation, and comparison capabilities.
Both tools lack proper MCP schema definitions. The 'hunt' and 'consult' tools are defined as Next.js API routes (app/api/hunt/route.ts, app/api/consult/route.ts), not as MCP tool registrations. No explicit tool registration with JSON Schema input/output definitions is visible in the provided source. Tool descriptions are present but generic. Input schemas show only a 'query' string parameter with minimal constraint. No output schemas documented. No error handling guidance. This is a Next.js web application, not an MCP server.
AI consultant that analyzes user request and recommends the single best tool from the database that solves it.
Deep search for AI tools using Tavily search and Groq LLM analysis. Searches for tools matching user query, analyzes results, and upserts new tools to database.
No MCP tool registration visible. Tools are Next.js API routes, not MCP-compliant tool definitions. No JSON Schema with proper type constraints, enums, or parameter descriptions beyond 'query'.
Output schemas completely undocumented. No specification of what 'hunt' returns (tool list structure, fields, pagination?) or what 'consult' returns (single tool recommendation format?).
Input parameter 'query' lacks constraints. No enum, pattern, length limits, or format specification. Description does not explain expected query syntax or examples of valid queries.
No error handling guidance. Tools call external APIs (Tavily, Groq, Supabase) but no documented error responses, retry logic, or recovery instructions for LLMs.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 33 | 2026-07-28+ | v2 |
'hunt' tool modifies database (upserts tools) but description does not explicitly state this is a write operation with side effects. LLMs cannot determine idempotency or retry safety.