Loading ToolBench
A state-of-the-art Model Context Protocol (MCP) server that provides seamless integration with Google's Gemini AI models. This server enables Claude Desktop and other MCP-compatible clients to leverage the full power of Gemini's advanced AI capabilities.
Server provides 6 tools with mostly complete schemas and descriptions. Naming follows verb-first convention (generate_text, analyze_image, count_tokens, list_models, embed_text, get_help). All tool descriptions are present and substantive (50-100+ chars). Input schemas are properly structured with JSON Schema types and most parameters include descriptions. However, there are gaps in output schema documentation, parameter constraint descriptions lack actionable detail, and error handling guidance is minimal. No tool annotations (readOnlyHint/destructiveHint) are visible in the code despite all tools being READ_ONLY risk. The server lacks confirmation patterns for sensitive operations and does not document output field references needed for tool chaining.
Analyze images using Gemini vision capabilities
Count tokens for a given text with a specific model
Generate embeddings for text using Google's embedding models
Generate text using Google Gemini with advanced features
Get help and documentation about available tools, models, and features
List available Gemini models with their capabilities and specifications
Output schemas are not documented. No description of what generate_text, analyze_image, or other tools return. LLMs cannot plan downstream tool calls without knowing response structure.
Tool annotations (readOnlyHint, destructiveHint, idempotentHint) are absent from all tools despite being flagged as READ_ONLY risk. This prevents agents from reasoning about side effects and retry safety.
Parameter descriptions lack actionable constraints. E.g. 'Temperature for generation (0-2)' states range but not guidance on when to use low vs high values. 'Specific Gemini model to use' does not explain capability differences or when to pick which model.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 64 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 32 | - | v1 |
analyze_image requires either imageUrl OR imageBase64 but does not declare these as mutually exclusive. Parameter descriptions do not clarify the dependency or what happens if both are provided.
No error recovery guidance. Error handling is present (MCPError, ValidationError classes) but error responses do not include actionable next steps. An LLM hitting an invalid model selection has no guidance on which models are valid beyond retrying.
get_help tool has an optional 'topic' parameter with enum values but no description of what each topic contains. An LLM cannot determine which topic to request without trial and error.
generate_text and analyze_image accept complex JSON string parameters (safetySettings, jsonSchema) as strings rather than structured objects. This forces LLMs to construct JSON strings manually, inviting syntax errors. Should accept structured input.
The conversationId parameter in generate_text hints at multi-turn support but no documentation explains how conversation context is managed, what happens if an invalid ID is passed, or how to list existing conversations.