MCP Gemini CrunchTools
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- AlicenseAqualityCmaintenanceMCP server for Google's Gemini API, enabling text, image, video, speech, embeddings, and deep research capabilities through a single tool set.10MIT
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- AlicenseNot gradedqualityCmaintenanceMCP server for Google Gemini that does not require an API key, combining a CLI backend for Q\&A, research, and analysis with a web UI backend for image and video generation.11 npm2MIT
- AlicenseAqualityBmaintenanceMCP server for AI-powered research using Gemini. Provides fast grounded web search, deep autonomous research, URL extraction, and session management.627 PyPI9MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that lets AI assistants use Google's Gemini models. Generate text, analyze images, review code, and more — with support for multi-turn conversations and web-grounded answers.58 npm1MIT
- AlicenseAqualityDmaintenanceA dedicated server that wraps Google's Gemini AI models in a Model Context Protocol (MCP) interface, allowing other LLMs and MCP-compatible systems to access Gemini's capabilities like content generation, function calling, chat, and file handling through standardized tools.1612 npm36MIT
TDQS
Scored across 39 tools
Multiple tools have heavily overlapping purposes: gemini_query_tool, gemini_brainstorm_tool, and the various gemini_analyze_* tools all accept free-form text prompts to Gemini, making it nearly impossible to choose correctly. Pairs like gemini_generate_image_with_input_tool vs gemini_start_image_edit_tool, gemini_summarize_tool vs gemini_summarize_pdf_tool, and especially gemini_youtube_tool vs gemini_youtube_summary_tool create genuine selection ambiguity.
The gemini_ prefix and _tool suffix are consistent, but the internal structure is a jumble: verb_noun (summarize_pdf, query_cache) mixes with noun_verb (imagen_generate), verb-only (speak, extract), and noun-only (youtube, structured) forms. The adjacency of generate_image, generate_image_with_input, and imagen_generate uses three different orderings for the same concept with no predictable rule.
At 39 tools, this far exceeds a well-scoped surface, but the breadth might justify it since the server spans text, images, video, audio, documents, research, caching, and code execution. However, most of these domains don't need 3-6 near-synonyms each; the server could easily be consolidated to ~20 distinct capabilities without losing functionality.
For a Gemini API wrapper, the coverage is remarkably thorough: generation, editing, analysis, video, TTS/voice, research, caching, structured output, and code execution are all represented. Minor gaps exist (e.g., no way to list or cancel video operations beyond check, no delete for research sessions, no way to select a voice when calling speak despite list_voices existing), but these are edge cases rather than workflow-breaking holes.