This server has clear tool naming conventions and comprehensive parameter schemas. All tools have descriptions and input schemas with type definitions. However, output schemas are not documented, error handling lacks recovery guidance, and tool descriptions could be more specific about when and why to use each tool. The server follows good naming patterns (verb_noun: generate_text, analyze_image, list_models, code_review) and includes tool annotations. Parameter descriptions are present but some lack clarity on constraints and dependencies.
Analyze an image using Gemini vision. Provide either imageUrl or imageBase64.
Review a code diff using Gemini. Returns feedback on bugs, style, and improvements.
Generate text using Google Gemini
List available Gemini models
Output schemas are not documented. LLMs cannot plan downstream tool chaining or know what fields to extract from responses.
Error handling lacks recovery guidance. Errors are returned as plain text (see handlers.ts fail() function) without actionable next steps or classification (retryable vs user-fixable vs fatal).
generate_text description does not distinguish it from analyze_image or code_review when to use. Missing clarification on tool selection context.
analyze_image parameter constraint 'Provide either imageUrl or imageBase64' is stated in description but not enforced in schema (no oneOf/anyOf). LLMs may pass both or neither.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 58 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 43 | 2024-11-05+ | v1 |
Parameter descriptions lack explicit format/constraint guidance. E.g., conversationId in generate_text has no format specification; temperature describes range (0-2) but no guidance on typical values or impact; safetySettings items lack required field markers.
list_models tool description is only 28 characters ('List available Gemini models'), below the 34-character p10 baseline. Lacks context on when/why to call it or what structure is returned.