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#analysis

246 results found

MCP-MESSENGER

**SlashMCP** is a production-grade AI workspace that connects LLMs to real-world data and tools through an intuitive chat interface. Built on the Model Context Protocol (MCP), it enables seamless interaction with multiple AI providers (OpenAI, Claude, Gemini) while providing powerful capabilities for document analysis, financial data queries, web scraping, and multi-agent workflow orchestration. ### Key Features: - **Multi-LLM Support**: Switch between GPT-4, Claude, and Gemini at runtime—no restart needed - **Smart Command Autocomplete**: Type `/` to discover and execute MCP server commands instantly - **Document Intelligence**: Drag-and-drop documents with automatic OCR extraction and vision analysis - **Financial Data Integration**: Real-time stock quotes, charts, and prediction market data via Alpha Vantage and Polymarket - **Browser Automation**: Web scraping and navigation using Playwright MCP - **Multi-Agent Orchestration**: Intelligent routing with specialized agents for command discovery, tool execution, and response synthesis - **Dynamic MCP Registry**: Add and use any MCP server on the fly without code changes - **Voice Interaction**: Browser-based transcription and text-to-speech support ### Use Cases: - Research and analysis workflows - Document processing and extraction - Financial market monitoring - Web data collection and comparison - Multi-step task automation **Live Demo:** [ slashmcp.vercel.app ]( https://slashmcp.vercel.app ) **GitHub:** [ github.com/mcpmessenger/slashmcp ]( https://github.com/mcpmessenger/slashmcp ) **Website:** [ slashmcp.com](https://slashmcp.com )

Vision Mcp Server | 图片分析 Mcp

This MCP addresses the visual recognition limitations of text-based models by enabling accurate image description and identification, making it excellent for AI-assisted reference design interface analysis. It currently supports dropping links into the dialog box or placing images in the project folder for recognition. The tool can be integrated with MCP platforms like Claude Code, Cline, and Trae. Beyond programming applications, it also provides visual recognition capabilities for models that lack native image processing functionality. For visual models, users can select their preferred model from ModelScope community and replace it during MCP configuration setup. 📱 Daily Use Cases: Send screenshots to directly identify errors or issues Share image links or place screenshots in the project folder for AI-assisted layout optimization Submit product image links to generate promotional copy 该mcp可以解决文字模型图片识别的视觉的问题,可以准确识别描述图片,用来给AI看参考设计界面很nice~ 目前支持丢链接到对话框,以及把图片放到项目文件夹进行识别。 支持加入到Claude Code,Cline和Trae等mcp工具中。 除了编程外,如果你使用的模型本身不支持视觉图片识别,也可以使用~ 视觉模型可以自己去魔搭社区选一个自己喜欢的,在填写mcp配置的时候替换即可 📱 日常使用场景 - 截图发过去,直接告诉哪里出错了 - 丢过去一个图片链接或者截图放到项目文件夹内,让AI帮忙优化布局 - 发个产品图链接,让AI写推广文案

Codegraph Mcp

# Transform any MCP-compatible LLM into a codebase expert through semantic intelligence A blazingly fast graphRAG implementation. 100% Rust for indexing and querying large codebases with natural language. Supports multiple embedding providers: modes cpu (no graph just AST parsing), onnx (blazingly fast medium quality embeddings with Qdrant/all-MiniLM-L6-v2-onnx) and Ollama (time consuming SOTA embeddings with hf.co/nomic-ai/nomic-embed-code-GGUF:Q4_K_M). I would argue this is the fastest codebase indexer on the Github atm. Includes a Rust SDK made stdio MCP server so that your agents can query the indexed codegraph with natural language and get deep insights from your codebase before starting development or making changes. Currently supports typescript, javascript, rust, go, Python and C++ codebases. 📊 Performance Benchmarking (M4 Max 128GB) Production Codebase Results (1,505 files, 2.5M lines, Python, Javascript, Typescript and Go) 🎉 INDEXING COMPLETE! 📊 Performance Summary ┌───────────────. ─┐ │ 📄 Files: 1,505 indexed │ │ 📝 Lines: 2,477,824 processed │ │ 🔧 Functions: 30,669 extracted │ │ 🏗️ Classes: 880 extracted │ │ 💾 Embeddings: 538,972 generated │ └───────────────. ─┘ Embedding Provider Performance Comparison Provider Time Quality Use Case 🧠 Ollama nomic-embed-code ~15-18h SOTA retrieval accuracy Production, smaller codebases ⚡ ONNX all-MiniLM-L6-v2 32m 22s Good general embeddings Large codebases, lunch-break indexing 📚 LEANN ~4h The next best thing I could find in Github CodeGraph Advantages ✅ Incremental Updates: Only reprocess changed files (LEANN can't do this) ✅ Provider Choice: Speed vs. quality optimization based on needs ✅ Memory Optimization: Automatic optimisations based on your system ✅ Production Ready: Index 2.5M lines while having lunch Read the README.md carefully the installation is complex and requires you to download the embedding model in onnx format and Ollama and setting up multiple environment variables (I would recommend setting these in your bash configuration)

Codegraph Rust

🎯 Overview CodeGraph is a powerful CLI tool that combines MCP (Model Context Protocol) server management with sophisticated code analysis capabilities. It provides a unified interface for indexing projects, managing embeddings, and running MCP servers with multiple transport options. All you now need is an Agent(s) to create your very own deep code and project knowledge synthehizer system! Key Capabilities 🔍 Advanced Code Analysis: Parse and analyze code across multiple languages using Tree-sitter 🚄 Dual Transport Support: Run MCP servers with STDIO, HTTP, or both simultaneously 🎯 Vector Search: Semantic code search using FAISS-powered vector embeddings 📊 Graph-Based Architecture: Navigate code relationships with RocksDB-backed graph storage ⚡ High Performance: Optimized for large codebases with parallel processing and batched embeddings 🔧 Flexible Configuration: Extensive configuration options for embedding models and performance tuning RAW PERFORMANCE ✨✨✨ 170K lines of rust code in 0.49sec! 21024 embeddings in 3:24mins! On M3 Pro 32GB Qdrant/all-MiniLM-L6-v2-onnx on CPU no Metal acceleration used! Parsing completed: 353/353 files, 169397 lines in 0.49s (714.5 files/s, 342852 lines/s) [00:03:24] [########################################] 21024/21024 Embeddings complete ✨ Features Core Features Project Indexing Multi-language support (Rust, Python, JavaScript, TypeScript, Go, Java, C++) Incremental indexing with file watching Parallel processing with configurable workers Smart caching for improved performance MCP Server Management STDIO transport for direct communication HTTP streaming with SSE support Dual transport mode for maximum flexibility Background daemon mode with PID management Code Search Semantic search using embeddings Exact match and fuzzy search Regex and AST-based queries Configurable similarity thresholds Architecture Analysis Component relationship mapping Dependency analysis Code pattern detection Architecture visualization support

Screenmonitormcp

ScreenMonitorMCP - Revolutionary AI Vision Server Give AI real-time sight and screen interaction capabilities ScreenMonitorMCP is a revolutionary MCP (Model Context Protocol) server that provides Claude and other AI assistants with real-time screen monitoring, visual analysis, and intelligent interaction capabilities. This project enables AI to see, understand, and interact with your screen in ways never before possible. Why ScreenMonitorMCP? Transform your AI assistant from text-only to a visual powerhouse that can: Monitor your screen in real-time and detect important changes Click UI elements using natural language commands Extract text from any part of your screen Analyze screenshots and videos with AI Provide intelligent insights about screen activity Core Features Smart Monitoring System start_smart_monitoring() - Enable intelligent monitoring with configurable triggers get_monitoring_insights() - AI-powered analysis of screen activity get_recent_events() - History of detected screen changes stop_smart_monitoring() - Stop monitoring with preserved insights Natural Language UI Interaction smart_click() - Click elements using descriptions like "Save button" extract_text_from_screen() - OCR text extraction from screen regions get_active_application() - Get current application context Visual Analysis Tools capture_and_analyze() - Screenshot capture with AI analysis record_and_analyze() - Video recording with AI analysis query_vision_about_current_view() - Ask AI questions about current screen System Performance get_system_metrics() - Comprehensive system health dashboard get_cache_stats() - Cache performance statistics optimize_image() - Advanced image optimization simulate_input() - Keyboard and mouse simulation

Video Jungle Video Editor Mcp Server

See a demo here: https://www.youtube.com/watch?v=KG6TMLD8GmA Upload, edit, search, and generate videos from everyone's favorite LLM and Video Jungle. You'll need to sign up for an account at Video Jungle in order to use this tool, and add your API key. The server implements an interface to upload, generate, and edit videos with: Custom vj:// URI scheme for accessing individual videos and projects Each project resource has a name, description Search results are returned with metadata about what is in the video, and when, allowing for edit generation directly Tools The server implements a few tools: add-video Add a Video File for analysis from a URL. Returns an vj:// URI to reference the Video file create-videojungle-project Creates a Video Jungle project to contain generative scripts, analyzed videos, and images for video edit generation edit-locally Creates an OpenTimelineIO project and downloads it to your machine to open in a Davinci Resolve Studio instance (Resolve Studio must already be running before calling this tool.) generate-edit-from-videos Generates a rendered video edit from a set of video files generate-edit-from-single-video Generate an edit from a single input video file get-project-assets Get assets within a project for video edit generation. search-videos Returns video matches based upon embeddings and keywords update-video-edit Live update a video edit's information. If Video Jungle is open, edit will be updated in real time.