Loading ToolBench
This server has three READ_ONLY tools with no input parameters. While naming is clear and descriptions are present, the schemas are completely absent, there are no explicit input schema definitions visible in the source code. The tools are registered via @mcp.tool() decorators in FastMCP, but no JSON Schema is declared. Output schemas are not documented. Error handling is absent, tools will fail silently or with unstructured exceptions if APIs are down or return unexpected data. The descriptions are minimal (10-20 chars) and lack actionable context for LLM selection. No tool annotations (readOnlyHint, etc.) are present despite all three being read-only. This is a minimal prototype-grade implementation.
Returns the top 5 trending movies from iTunes.
Returns the top 5 trending coins from CoinGecko.
Returns the top 5 trending active stories from Hacker News.
No input schemas visible. Tools registered via @mcp.tool() but no explicit JSON Schema definitions present in source code.
Output schemas not documented. Tools return list[str] but LLMs cannot see the structure of the response or plan downstream calls. No documentation of what each string represents or how many items are expected.
Descriptions are extremely minimal (10-20 characters). 'Returns the top 5 trending...' lacks actionable context for LLM selection. No explanation of WHEN to use each tool vs alternatives, what the values mean, or how frequently data updates.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 42 | 2026-07-28+ | v2 |
No error handling or recovery guidance. Tools call external APIs (Hacker News, CoinGecko, iTunes) without timeouts, retry logic, or error messages. If APIs are down, the agent receives a raw Python exception. No guidance on what to do next.
No tool annotations. All three tools are read-only, but readOnlyHint is not set. This forces LLMs to infer safety from descriptions, increasing hallucination risk.
Responses are unstructured lists of strings. LLMs cannot extract metadata (source, timestamp, relevance score, URL). Rich structured output enables multi-step compositions (e.g., 'Get trends, then search for details on top result').
No pagination or limit parameters. Each tool always returns exactly 5 items. If an API starts returning 100+ items, the context window is blown. No next_cursor or offset mechanism.