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The server defines 2 tools with basic descriptions and partial parameter documentation. However, critical gaps in schema completeness, output documentation, and error recovery guidance significantly limit production readiness. Tool naming follows verb conventions, but descriptions are under 100 characters (below the 194-char baseline for A+ tools). Most parameters lack formal type constraints (enums where appropriate). Output schemas are not documented. Error handling returns plain strings rather than structured recovery guidance.
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Output schemas not documented. Neither tool declares what fields the LLM should expect in responses. 'ask_gemini' returns a formatted string prefixed with emoji/headers; 'server_info' returns a formatted string. LLMs cannot plan downstream calls or extract structured data when output is undocumented.
Temperature parameter lacks range constraint validation. Description states '0.0-1.0' but no min/max are enforced in schema; LLMs can pass invalid values like 2.5 or -1.
Error responses are plain strings with no recovery guidance. Errors like 'Gemini not available', 'Rate limit exceeded', or 'Content filtered' are returned as bare messages. Per pattern:recovery-guide, errors must tell the LLM what to do next (e.g., 'Rate limit hit, wait 60s and retry, or call server_info() to check status').
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 44 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 32 | - | v1 |
No error classification. Error responses do not signal whether errors are retryable, user-fixable, or fatal. An LLM cannot decide whether to retry, ask the user, or give up without explicit classification.
ask_gemini tool chains poorly. It accepts a 'context' string parameter and a 'persona' string, but these are free-form text with no validation, examples, or guidance. Persona should suggest valid examples or accept an enum (e.g., 'senior_architect', 'security_expert', 'data_analyst').
server_info returns formatted plain text with no structured schema. Output is human-readable but not machine-parseable. LLMs cannot reliably extract gemini_available status or model name for programmatic decisions.
ask_gemini parameter descriptions are terse (e.g., 'Temperature for response (0.0-1.0)' is 37 chars vs. baseline 72 chars). Should say 'Controls randomness of responses: 0 = deterministic, 1 = creative. Use 0 for factual tasks, 0.7+ for creative writing.'