This server has critical definition quality issues. Only 2 unique tools exist (earthquake_probability, next_volcanic_eruption), but the tool list duplicates them. Descriptions are present but minimal (10-40 chars, below the 50-200 char LLM-optimized baseline). earthquake_probability has a parameter schema with type and description, but next_volcanic_eruption accepts no parameters. No output schemas are documented. No error handling guidance. No tool annotations (readOnlyHint, destructiveHint, idempotentHint). The fastmcp.py implementation shows async functions but no explicit schema registration visible in the code, tool definitions appear inferred from decorators rather than explicitly registered with full schemas.
For a given location, return the earthquake probability.
For a given location, return the earthquake probability.
Get the location of the next volcanic eruption.
Get the location of the next volcanic eruption.
Duplicate tool definitions in tool list (earthquake_probability and next_volcanic_eruption each listed twice). Indicates either a registration error or incomplete tool discovery.
next_volcanic_eruption has no input schema (empty {}). Tool returns a string but output schema is not documented.
Descriptions are too short (10-40 chars vs. 50-200 char baseline). 'For a given location, return the earthquake probability.' lacks context on when to use it vs. similar tools, prerequisites, or return format. 'Get the location of the next volcanic eruption.' is vague about data freshness and accuracy.
No output schemas documented. LLMs cannot plan downstream calls or extract fields. earthquake_probability returns float; next_volcanic_eruption returns string, neither is formally declared in tool metadata.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 41 | <=2025-11-25 | v2 |
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). Both tools are read-only but this is not declared in the tool definition, forcing LLMs to infer safety from descriptions alone.