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A comprehensive MCP server for Google Gemini AI with Smart Tool Intelligence and self-contained preferences. Provides image generation, chat, audio transcription, video analysis, code execution, and image editing capabilities.
This MCP server has fundamental definition quality gaps across nearly all tools. While tool names follow basic action-verb conventions (gemini-image-generation, gemini-chat, etc.), the implementation suffers from: (1) Missing or incomplete descriptions for many parameters, baseline is 100% of A+ tool params have descriptions, this server achieves ~40%; (2) Input schemas present but poorly documented, parameter descriptions are minimal and lack constraint information; (3) Vague/missing descriptions for several tools that do not explain WHEN to use them or WHAT context they require; (4) No output schema documentation visible, critical for LLM planning; (5) No error handling or recovery guidance. The server appears functional but falls well below production quality. Average per-tool score: 38/100, typical for community/experimental MCP servers.
Generates advanced images using Gemini 2.5 Flash with reference images and specialized modes
Analyzes images using Gemini AI for content understanding, OCR, and object detection
Analyzes video files using Gemini AI for content understanding and description
Chat interface with Gemini AI that learns user preferences and applies Smart Tool Intelligence enhancements
Executes code snippets using Gemini AI for analysis and execution support
Edits and enhances existing images using Gemini AI
Generates images using Google Gemini AI based on text prompts
Missing output schema documentation across all 10 tools. LLMs cannot plan downstream calls or structure reasoning without knowing return types and field names.
Parameter descriptions are minimal and lack constraint information. 'Text description of the image to generate' (prompt in gemini-image-generation) does not explain: What length? What content is forbidden? What triggers rate limits? Baseline: 100% of A+ tools document constraints.
No error handling or recovery guidance. No tool indicates what errors are retryable, user-fixable, or fatal. A failed gemini-execute-code call provides no actionable next step for the agent.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 42 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 25 | 2024-11-05+ | v1 |
Generates images using Nano Banana Pro (Gemini 3 Pro Image) with up to 14 reference images and 4K resolution support
Transcribes audio files to text using Gemini AI with support for verbatim mode and filler word handling
Uploads files to Google's File API for use with Gemini models
gemini-execute-code (IRREVERSIBLE risk) lacks dry-run support and confirmation step. This is a destructive operation that should support agent confirmation before execution. Pattern: confirmation-request.
WHEN to use guidance is missing. Tool descriptions do not explain selection criteria. When should an LLM choose gemini-chat over gemini-analyze-image? What context makes gemini-advanced-image preferable to gemini-image-generation? Baseline: 100% of A+ tools explain WHEN to use them.
No input validation rules documented. 'Base64 encoded image data' (imageBase64 in gemini-image-editing) lacks size limits, format constraints, or example. LLMs will guess: will a 100MB image work? What about a GIF?
Vague enum labels. gemini-nano-banana-pro mode enum includes 'fusion', 'consistency', 'targeted_edit', 'template', 'standard' with no description of what each does. LLMs cannot reason about which to select.
Inconsistent parameter naming and field mapping. 'aspect_ratio' in gemini-nano-banana-pro uses snake_case while other params use camelCase (referenceImages, mimeType). This forces LLM reasoning about field naming conventions and invites mapping errors.