Loading ToolBench
Collection of MCP servers providing integrations with Gemini, Grok, Vertex AI, Azure AI Foundry, ElevenLabs, and other AI/creative platforms
This MCP server presents 16 tools across Gemini, Vertex AI, and image/video generation APIs. While schemas ARE visible and mostly well-formed, descriptions are inconsistent in depth and quality. Most tools have basic parameter documentation, but many lack the WHEN-to-use guidance and prerequisite warnings that LLMs need for optimal selection. Several tools have generic or vague descriptions under 100 characters. Parameter naming is mostly verb_noun compliant, but some parameters lack clear format/constraint guidance. Output schemas are largely undocumented, LLMs cannot see what fields they will receive. Error handling is minimal to absent. Composition is reasonable (single-responsibility tools), but several tools that accept 'prompt' or 'analysis_type' parameters could benefit from enum constraints and deeper descriptions. Overall, definitions meet a baseline but fall short of production-grade quality expected in patterns like pattern:tool-description and pattern:constrained-input.
PDF/document analysis using Gemini 3 Pro
Vision analysis of base64 encoded images using Gemini 3 Pro
Text analysis: sentiment, summary, entities, key-points, or general
Collaborative brainstorming with Gemini 3 Pro (thinking=high)
Edit existing image via Nano Banana Pro (Gemini 3 Pro Image)
Generate images using Nano Banana Pro (Gemini 3 Pro Image) via generateContent with IMAGE modality
Output schemas are universally undocumented. LLMs cannot see what fields they receive from these tools, preventing them from planning downstream tool calls or extracting specific data. For example, gemini-analyze-text returns sentiment/summary/entities, but the LLM doesn't know the field names, types, or structure.
Multiple tools have generic descriptions under 100 characters without WHEN-to-use guidance. For example, 'General reasoning with Gemini 3 Pro or Flash' doesn't explain why an LLM should choose gemini-query vs gemini-brainstorm vs vertex_chat. LLMs resolve this ambiguity by reasoning about intent, wasting tokens and increasing error likelihood.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 53 | 2026-07-28+ | v2 |
| 2026-04-21 | F | 15 | - | v1 |
Query with Google Search grounding for real-time data
Optimize prompts for AI image generation using Gemini 3 Flash
Generate images with Imagen 4 (top image model). Uses separate generateImages API.
General reasoning with Gemini 3 Pro or Flash. Use pro for deep reasoning, flash for speed.
Analyze URLs with Gemini url_context tool
Generate video with Veo 3.1 (async polling, may take minutes)
Brainstorm ideas using Vertex AI.
Chat with Vertex AI models (Gemini, Claude via Vertex).
Code review using Vertex AI.
Deep reasoning using Vertex AI thinking model.
Parameter constraints are insufficiently documented in descriptions. 'language' in vertex_code_review is a free-form string with no enum; LLMs hallucinate invalid languages. 'num_ideas' in vertex_brainstorm lacks a range constraint; LLMs could request 1000 ideas. Constraints should appear in descriptions (backup for LLMs) AND as JSON Schema enums/minValue/maxValue.
Image and document tools lack format/size constraints in descriptions. 'image_data' in gemini-analyze-image and 'document_data' in gemini-analyze-document don't specify max file size, supported formats (JPEG? WebP?), or dimensions. LLMs guess wrong and trigger API errors without recourse guidance.
Overlapping tool functionality creates ambiguity. 'gemini-query', 'vertex_chat', and 'gemini-grounded-query' all accept a prompt. Why would an LLM choose one over the others? Descriptions don't clarify: gemini-query is offline reasoning; vertex_chat is generic; grounded-query adds web search. Without this guidance, LLMs waste reasoning cycles. Similarly, gemini-brainstorm vs vertex_brainstorm are indistinguishable.
Async/polling behavior is not explained. gemini-veo mentions 'async polling, may take minutes' but doesn't document HOW the LLM should poll (retry the same call? a separate status_check tool?) or HOW LONG to wait before timing out. In STDIO transport, there is no background polling mechanism; the LLM must handle this manually or the call hangs.
Error handling is absent or minimal. No tool describes what errors can occur, what they mean, or what the LLM should do. For example, if a vision tool rejects a non-image base64 string, should the LLM retry, ask the user, or try a different tool? Without guidance, the LLM stalls.