MCP ServersMCP Servers

Awesome MCP Servers for Developer Tools

8735 MCP Servers Found

Prompt Optimizer Local

Prompt Optimizer

Transform AI prompts with intelligent context detection, 50+ professional optimization techniques, and seamless team collaboration. Integrated with Claude Desktop, Cursor, Windsurf, and 17+ MCP clients. • **100% Local Processing** - All prompt optimization is done on your machine, ensuring complete privacy and confidentiality. • **Offline Capability** - Works without an internet connection, making it ideal for secure or air-gapped environments. • **Advanced Local Prompt Intelligence** - Sophisticated content analysis and optimization performed directly on your machine, including context-aware optimization for debugging and technical prompts. • **Cross-Platform Support** - Universal compatibility for Windows, macOS, and Linux. • **Binary Integrity Verification** - SHA256 hash validation ensures the integrity of the local server. • **Technical Parameter Preservation** - Maintains code blocks, API calls, and other technical details during optimization, including parameters like --ar and --v. • **Debugging Scenario Detection** - Context-aware optimization tailored for debugging and technical prompts.

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a month ago

Prompt Optimizer

nivlewd1

Our system automatically analyzes your prompts to detect whether they're for image generation (like Midjourney), LLM interaction, or technical automation, then applies the most effective optimization techniques for that context. • **100% Local Processing** - All prompt optimization is done on your machine, ensuring complete privacy and confidentiality. • **Offline Capability** - Works without an internet connection, making it ideal for secure or air-gapped environments. • **Advanced Local Prompt Intelligence** - Sophisticated content analysis and optimization performed directly on your machine, including context-aware optimization for debugging and technical prompts. • **Cross-Platform Support** - Universal compatibility for Windows, macOS, and Linux. • **Binary Integrity Verification** - SHA256 hash validation ensures the integrity of the local server. • **Technical Parameter Preservation** - Maintains code blocks, API calls, and other technical details during optimization, including parameters like --ar and --v. • **Debugging Scenario Detection** - Context-aware optimization tailored for debugging and technical prompts.

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a month ago

Sonatype Dependency Management Mcp Server

sonatype

The Sonatype MCP Server enables AI assistants to access Sonatype's comprehensive dependency intelligence directly within your development workflow. By integrating with the Model Context Protocol, your AI assistant can help you make informed decisions about dependencies, identify security risks, and maintain compliance — all without leaving your IDE. Key Features Component Version Selection - Select the best version the first time, without the side quest Security Vulnerability Scanning - Identify known vulnerabilities in your project dependencies License Compliance Checking - Ensure your dependencies meet your organization's license policies Dependency Health Analysis - Get insights into dependency quality, maintenance status, and risk factors Real-time Security Advisories - Stay informed about the latest security threats affecting your dependencies Remediation Guidance - Receive actionable recommendations to fix vulnerabilities and compliance issues

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a month ago

Smart Ai Bridge

Platano78

Smart AI Bridge is a production-ready Model Context Protocol (MCP) server that orchestrates AI-powered development operations across multiple backends with automatic failover, smart routing, and advanced error prevention capabilities. Key Features 🤖 Multi-AI Backend Orchestration Pre-configured 4-Backend System: 1 local model + 3 cloud AI backends (fully customizable - bring your own providers) Fully Expandable: Add unlimited backends via EXTENDING.md guide Intelligent Routing: Automatic backend selection based on task complexity and content analysis Health-Aware Failover: Circuit breakers with automatic fallback chains Bring Your Own Models: Configure any AI provider (local models, cloud APIs, custom endpoints) 🎨 Bring Your Own Backends: The system ships with example configuration using local LM Studio and NVIDIA cloud APIs, but supports ANY AI providers - OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, custom APIs, or local models via Ollama/vLLM/etc. See EXTENDING.md for integration guide. 🎯 Advanced Fuzzy Matching Three-Phase Matching: Exact (<5ms) → Fuzzy (<50ms) → Suggestions (<100ms) Error Prevention: 80% reduction in "text not found" errors Levenshtein Distance: Industry-standard similarity calculation Security Hardened: 9.7/10 security score with DoS protection Cross-Platform: Automatic Windows/Unix line ending handling 🛠️ Comprehensive Toolset 19 Total Tools: 9 core tools + 10 intelligent aliases Code Review: AI-powered analysis with security auditing File Operations: Advanced read, edit, write with atomic transactions Multi-Edit: Batch operations with automatic rollback Validation: Pre-flight checks with fuzzy matching support 🔒 Enterprise Security Security Score: 9.7/10 with comprehensive controls DoS Protection: Complexity limits, iteration caps, timeout enforcement Input Validation: Type checking, structure validation, sanitization Metrics Tracking: Operation monitoring and abuse detection Audit Trail: Complete logging with error sanitization 🏆 Production Ready: 100% test coverage, enterprise-grade reliability, MIT licensed 🚀 Multi-Backend Architecture Flexible 4-backend system pre-configured with 1 local + 3 cloud backends for maximum development efficiency. The architecture is fully expandable - see EXTENDING.md for adding additional backends. 🎯 Pre-configured AI Backends The system comes with 4 specialized backends (fully expandable via EXTENDING.md): Cloud Backend 1 - Coding Specialist (Priority 1) Specialization: Advanced coding, debugging, implementation Optimal For: JavaScript, Python, API development, refactoring, game development Routing: Automatic for coding patterns and task_type: 'coding' Example Providers: OpenAI GPT-4, Anthropic Claude, Qwen via NVIDIA API, Codestral, etc. Cloud Backend 2 - Analysis Specialist (Priority 2) Specialization: Mathematical analysis, research, strategy Features: Advanced reasoning capabilities with thinking process Optimal For: Game balance, statistical analysis, strategic planning Routing: Automatic for analysis patterns and math/research tasks Example Providers: DeepSeek via NVIDIA/custom API, Claude Opus, GPT-4 Advanced, etc. Local Backend - Unlimited Tokens (Priority 3) Specialization: Large context processing, unlimited capacity Optimal For: Processing large files (>50KB), extensive documentation, massive codebases Routing: Automatic for large prompts and unlimited token requirements Example Providers: Any local model via LM Studio, Ollama, vLLM - DeepSeek, Llama, Mistral, Qwen, etc. Cloud Backend 3 - General Purpose (Priority 4) Specialization: General-purpose tasks, additional fallback capacity Optimal For: Diverse tasks, backup routing, multi-modal capabilities Routing: Fallback and general-purpose queries Example Providers: Google Gemini, Azure OpenAI, AWS Bedrock, Anthropic Claude, etc. 🎨 Example Configuration: The default setup uses LM Studio (local) + NVIDIA API (cloud), but you can configure ANY providers. See EXTENDING.md for step-by-step instructions on integrating OpenAI, Anthropic, Azure, AWS, or custom APIs. 🧠 Smart Routing Intelligence Advanced content analysis with empirical learning: // Smart Routing Decision Tree if (prompt.length > 50,000) → Local Backend (unlimited capacity) else if (math/analysis patterns detected) → Cloud Backend 2 (analysis specialist) else if (coding patterns detected) → Cloud Backend 1 (coding specialist) else → Default to Cloud Backend 1 (highest priority) Pattern Recognition: Coding Patterns: function|class|debug|implement|javascript|python|api|optimize Math/Analysis Patterns: analyze|calculate|statistics|balance|metrics|research|strategy Large Context: File size >100KB or prompt length >50,000 characters

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2 months ago

Icons8 MCP Server

Icons8

Icons8 MCP Server gives AI coding environments instant access to 368,865+ Icons8 icons across 116 design styles. Connect your Claude, Cursor, Windsurf, VS Code, or any SSE-capable MCP host to stream high-res PNGs for free or unlock full SVG delivery with an API key—ideal for prototyping, production code, and rapid UI experiments. Use cases - Replace entire icon sets in existing projects with consistent, on-trend styles. - Add icons to bullet lists, feature highlights, process steps, and dashboards without leaving your IDE. - Prototype quickly with free PNG previews, then upgrade to SVGs for production-ready assets. - Build AI-assisted workflows that search, preview, and drop icons directly into your code. - Showcase interactivity with ready-made demos such as falling emojis, sci-fi dashboards, and product catalogs.

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2 months ago

Mcp Datadog Server

ClaudioLazaro

MCP Datadog Server Servidor MCP (Model Context Protocol) completo e robusto para integração com APIs do Datadog Um servidor MCP de produção que oferece 351 tools para interagir com todas as APIs do Datadog através de LLMs, incluindo operações CRUD completas, tools curadas e ferramentas geradas automaticamente do schema. Node.js MCP License 🚀 Características Principais 📊 Tools Disponíveis (351 total) 9 Tools Curadas 🎯 - Handcrafted, otimizadas para casos específicos 25 Tools CRUD ⚡ - Operações CREATE, READ, UPDATE, DELETE para recursos principais 319 Tools Geradas 🔧 - Geradas automaticamente do schema oficial do Datadog 🔍 Recursos Avançados ✅ Autodescoberta de Schema - LLMs descobrem parâmetros automaticamente ✅ Validação Robusta - Zod schemas com validação completa ✅ Progress Tracking - Acompanhamento em tempo real para operações longas ✅ Error Handling - Tratamento inteligente de erros e retry automático ✅ CLI Rica - Interface completa para gestão e debugging 🛡️ Conformidade MCP ✅ 100% Compatível com TypeScript SDK oficial ✅ JSON Schema completo para todas as tools ✅ Metadata Annotations detalhadas ✅ Type Safety com validação Zod

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2 months ago

Codegraph Mcp

Jakedismo

# 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)

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2 months ago

Codegraph Rust

Jakedismo

🎯 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

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3 months ago

Scalekit

Scalekit Model Context Protocol (MCP) server provides comprehensive tools for managing environments, organizations, users, connections, and workspace operations. Built for developers who want to connect their AI tools to Scalekit context and capabilities based on simple natural language queries. This MCP server enables AI assistants to interact with Scalekit’s identity and access management platform through a standardized set of tools. It provides secure, OAuth-protected access to manage environments, organizations, users, authentication connections, and more. Features Environment management and configuration Organization and user management Workspace member administration OIDC connection setup and management MCP server registration and configuration Role and scope management Admin portal link generation Configuration The Scalekit MCP server can be configured to support OAuth for compatible clients. If your MCP Client doesn’t support OAuth based authorization for MCP Servers, you can still use the Scalekit MCP server with the mcp-remote acting as a local proxy to add OAuth support. ## using OAuth: { "servers": { "scalekit": { "type": "http", "url": "https://mcp.scalekit.com/" } } } ## using mcp-remote: { "mcpServers": { "scalekit": { "command": "npx", "args": ["-y", "mcp-remote", "https://mcp.scalekit.com/"] } } }

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3 months ago

Pyghidra Mcp

clearbluejar

PyGhidra-MCP - Ghidra Model Context Protocol Server pyghidra-mcp is a command-line Model Context Protocol (MCP) server that brings the full analytical power of Ghidra, a robust software reverse engineering (SRE) suite, into the world of intelligent agents and LLM-based tooling. It bridges Ghidra’s ProgramAPI and FlatProgramAPI to Python using pyghidra and jpype, then exposes that functionality via the Model Context Protocol. MCP is a unified interface that allows language models, development tools (like VS Code), and autonomous agents to access structured context, invoke tooling, and collaborate intelligently. Think of MCP as the bridge between powerful analysis tools and the LLM ecosystem. With pyghidra-mcp, Ghidra becomes an intelligent backend—ready to respond to context-rich queries, automate deep reverse engineering tasks, and integrate into AI-assisted workflows. Yet another Ghidra MCP? Yes, the original ghidra-mcp is fantastic. But pyghidra-mcp takes a different approach: 🐍 No GUI required – Run entirely via CLI for streamlined automation and scripting. 🔁 Designed for automation – Ideal for integrating with LLMs, CI pipelines, and tooling that needs repeatable behavior. ✅ CI/CD friendly – Built with robust unit and integration tests for both client and server sessions. 🚀 Quick startup – Supports fast command-line launching with minimal setup. 📦 Project-wide analysis – Enables concurrent reverse engineering of all binaries in a Ghidra project 🤖 Agent-ready – Built for intelligent agent-driven workflows and large-scale reverse engineering automation. 🔍 Semantic code search – Uses vector embeddings (via ChromaDB) to enable fast, fuzzy lookup across decompiled functions, comments, and symbols—perfect for pseudo-C exploration and agent-driven triage.

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3 months ago

Docker Mcp Server

docker-mcp-server

Docker MCP Server A comprehensive Model Context Protocol (MCP) server that provides advanced Docker operations through a unified interface. This server combines 16 powerful Docker MCP tools with 25+ convenient CLI aliases to create a complete Docker workflow solution for developers, DevOps engineers, and system administrators. 🌟 What Makes Docker MCP Server Special Docker MCP Server is not just another Docker wrapper - it's a complete Docker workflow enhancement system designed to make Docker operations more intuitive, secure, and efficient: 🎯 Unified Interface MCP Protocol Integration: Seamlessly works with MCP-compatible tools and IDEs CLI Convenience: 25+ carefully crafted aliases for common Docker workflows Consistent API: All operations follow the same patterns and conventions Cross-Platform: Full support for Linux, macOS, and Windows environments 🔒 Security-First Design Docker-Managed Security: All password operations handled by Docker daemon for maximum security Zero Password Exposure: Passwords never appear in command history, process lists, or arguments Token Authentication Support: Full support for Personal Access Tokens and service accounts Registry Flexibility: Secure login to Docker Hub, AWS ECR, Azure ACR, Google GCR, and custom registries CI/CD Security: Secure stdin password input for automated deployment pipelines Permission Management: Proper handling of Docker daemon permissions and credential storage 🚀 Developer Experience Comprehensive Help System: Every command includes detailed documentation with --help Smart Defaults: Sensible default configurations for common use cases Error Prevention: Built-in safety checks and confirmation prompts for destructive operations Rich Output: Formatted, colored output with clear status indicators 📊 Advanced Operations Complete Container Lifecycle: From build to publish with comprehensive registry support Multi-Container Management: Docker Compose integration with service orchestration Registry Publishing: Advanced image publishing with multi-platform support and automated workflows Network & Volume Management: Advanced networking and storage operations System Maintenance: Intelligent cleanup tools with multiple safety levels Development Workflows: Specialized commands for development environments

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3 months ago

Master Mcp Server

Jakedismo

Master MCP Server Master MCP Server aggregates multiple MCP servers behind a single, secure endpoint. It provides configuration-driven module loading, unified capability discovery, request routing with resilience, and first-class OAuth flows for multi-backend authentication. # Highlights - Aggregates multiple MCP servers with tool/resource discovery and namespacing - OAuth support: master token pass-through, delegated provider flows, proxy refresh - Config-driven setup with JSON/YAML, schema validation, and secret resolution - Resilient routing: load-balancing, retries with backoff/jitter, circuit-breakers - Cross-platform: Node.js server and Cloudflare Workers runtime - Production-ready deployment: Docker, Cloudflare Workers, Koyeb - Testing strategy and CI-ready structure

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4 months ago

Xcode Mcp Server (drewster99)

drewster99

An MCP (Model Context Protocol) server for controlling and interacting with Xcode from AI assistants and LLMs like Claude Code, Cursor, Claude Desktop, LM Studio, etc. This server significantly improves the build cycle. Now Claude (or your favorite tool) can directly command Xcode to build your project. Because Xcode is building it directly (rather than xcodebuild command-line or similar), the build happens exactly the same way as when you build it in Xcode. Xcode-mcp-server returns relevant build errors or warnings back to your coding tool (like Cursor or Claude Code), so the LLM sees exactly the same errors you do. Included tool functions here - you don't really need to know this info because your coding LLM will get this info (and more details) automatically, but I've included it here for the curious: - version - Returns xcode-mcp-server's version string - get_xcode_projects - Finds all .xcodeproj and .xcworkspace projects in the given search_path. If search_path is empty, all paths to which the tool has been granted access are searched - get_project_hierarchy - Returns the path hierarchy of the project or workspace - get_project_schemes - Returns a list of build schemes for the specified project - build_project - Commands Xcode to build. This is the workhorse that builds your project again and again, returning success or build errors - run_project - Commands Xcode to run your project - get_build_errors - Returns most recent build errors from the given project - clean_project - Cleans build

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4 months ago

Maven Tools Mcp Server

arvindand

# Maven Tools MCP - AI-Powered Maven Central Intelligence MCP server providing instant, accurate dependency analysis for Maven, Gradle, SBT, Mill, and all JVM build tools. Get dependency intelligence that's faster and more reliable than web searches. ## Key Features - **Bulk Operations**: Analyze 20+ dependencies in one call (<500ms vs 60+ seconds manually) - **Universal JVM Support**: Works with Maven, Gradle, SBT, Mill using standard Maven coordinates - **Version Intelligence**: Automatic classification (stable/RC/beta/alpha) with stability filtering - **Age Analysis**: Classify dependencies as fresh/current/aging/stale with actionable insights - **Context7 Integration**: Smart documentation hints for complex upgrades and migrations - **Enterprise Performance**: <100ms cached responses, native images ## Perfect For - "Check all dependencies in this build file for latest versions" - "Show me only stable versions for production deployment" - "How old are my dependencies and which ones need attention?" - "Compare my current versions but only suggest stable upgrades" **Docker Installation**: One command setup with multi-architecture support. **GitHub**: https://github.com/arvindand/maven-tools-mcp

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4 months ago

Superjolt MCP Server - AI-Powered JavaScript Deployment Platform

scoritz

Superjolt is a Platform-as-a-Service (PaaS) that enables developers to deploy JavaScript applications instantly with a single command: npx superjolt deploy. Through its native Model Context Protocol (MCP) integration, developers can manage their entire deployment infrastructure using natural language via Claude Desktop. Instead of memorizing CLI commands or navigating complex dashboards, you can simply tell Claude what you want: "Show me all my running services," "Restart the API service," or "Set the database URL for production." Superjolt handles the complexities of deployment including automatic HTTPS, environment variables, real-time logs, and service management. The MCP integration transforms infrastructure management from a technical task into a conversation. Claude can access your deployment state, understand your infrastructure context, and execute complex workflows through simple commands. Whether you're deploying a new application, troubleshooting issues, or managing multiple environments, the MCP server provides Claude with the tools to list machines and services, start/stop/restart applications, manage environment variables, view logs, and handle the entire deployment lifecycle. This makes Superjolt ideal for developers who want to focus on building rather than configuring, offering the simplicity of serverless with the power of a full deployment platform—all controlled through natural language.

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4 months ago

Homey Mcp

pigmej

# HomeyPro MCP Server A Model Context Protocol (MCP) server for interacting with HomeyPro home automation systems. This server provides paginated access to devices, zones, and flows with comprehensive management capabilities. ## Features - **Device Management**: List, search, and control devices with full capability support - **Zone Management**: Browse zones and their associated devices - **Flow Management**: List and trigger automation flows - **System Management**: Get and update system configuration (location, address, language, units) - **AI-Powered Prompts**: Context-aware guidance for device control, troubleshooting, and automation - **Resource Caching**: Efficient data access with intelligent caching and stale data fallback - **Pagination Support**: Efficient handling of large datasets with cursor-based pagination - **Real-time Data**: Get current device states, capabilities, and insights - **Error Handling**: Comprehensive error handling with detailed error messages ## Installation ### Local Development 1. Clone the repository and navigate to the project directory: ```bash cd python-homey-mcp ``` 2. Install dependencies using uv: ```bash uv sync ``` ### Docker Pull the pre-built Docker image: ```bash docker pull ghcr.io/pigmej/python-homey-mcp:latest ``` No additional installation steps are required when using Docker. ## Configuration Before running the server, you need to configure your HomeyPro connection: ### Environment Variables Set the following environment variables: ```bash export HOMEY_API_URL="http://YOUR_HOMEY_IP_ADDRESS" export HOMEY_API_TOKEN="YOUR_PERSONAL_ACCESS_TOKEN" ``` ### Getting Your HomeyPro Token 1. Open the HomeyPro web interface 2. Go to Settings > General > API 3. Create a new Personal Access Token 4. Copy the token and set it as the `HOMEY_API_TOKEN` environment variable ### Finding Your HomeyPro IP Address You can find your HomeyPro's IP address in: - HomeyPro web interface: Settings > General > Network - Your router's admin panel - HomeyPro mobile app: More > Settings > General > Network ## Usage ### Running the Server #### Local Development ```bash uv run fastmcp run -t http --host 0.0.0.0 -p 4445 -l DEBUG main.py ``` #### Docker Container Run the MCP server in a Docker container: ```bash docker run -p 4445:4445 \ -e HOMEY_API_URL="http://YOUR_HOMEY_IP_ADDRESS" \ -e HOMEY_API_TOKEN="YOUR_PERSONAL_ACCESS_TOKEN" \ ghcr.io/pigmej/python-homey-mcp:latest ``` Or using docker-compose: ```yaml version: '3.8' services: python-homey-mcp: image: ghcr.io/pigmej/python-homey-mcp:latest ports: - "4445:4445" environment: - HOMEY_API_URL=http://YOUR_HOMEY_IP_ADDRESS - HOMEY_API_TOKEN=YOUR_PERSONAL_ACCESS_TOKEN ``` The server will start and connect to your HomeyPro instance. You'll see a connection confirmation message. But basically please yield to [FastMCP docs](https://gofastmcp.com/patterns/cli) ## AI-Powered Prompts The server provides context-aware prompts that help you interact with your HomeyPro system more effectively. These prompts analyze your current system state and provide tailored guidance. ### Available Prompts #### Device Control Assistant Provides structured guidance for controlling different types of devices in your HomeyPro system. - **Context**: Current device counts, online/offline status, available device types - **Guidance**: Control patterns for lighting, climate, security, and entertainment devices - **Best Practices**: Device status checking, capability usage, troubleshooting tips #### Device Troubleshooting Systematic diagnostic guidance for common HomeyPro device issues. - **System Health**: Overall device health percentage and status indicators - **Step-by-Step Process**: Structured troubleshooting workflow - **Device-Specific**: Targeted solutions for offline and unresponsive devices - **Advanced Diagnostics**: Network, performance, and system-level troubleshooting #### Device Capability Explorer Helps you discover and understand device capabilities without overwhelming detail. - **Capability Categories**: Control, sensor, and status capabilities - **Value Types**: Boolean, numeric, string, and enum capability formats - **Usage Patterns**: Common capability combinations and best practices - **Device Type Patterns**: Capability patterns for different device categories #### Flow Creation Assistant Structured guidance for creating HomeyPro automation flows. - **Flow Framework**: WHEN (trigger), AND (conditions), THEN (actions) structure - **Common Scenarios**: Security, comfort, energy, convenience, and safety automations - **System Context**: Available zones, device types, and existing flows - **Templates**: Ready-to-use flow templates for common use cases #### Flow Optimization Guidance for improving existing flow performance and reliability. - **Performance Analysis**: Flow execution patterns and optimization opportunities - **Resource Usage**: Device and system resource considerations - **Best Practices**: Flow organization, naming, and maintenance strategies #### Flow Debugging Systematic approach to diagnosing and fixing flow issues. - **Common Problems**: Flow execution failures, timing issues, device conflicts - **Diagnostic Tools**: Log analysis, condition testing, action verification - **Resolution Strategies**: Step-by-step debugging workflow #### System Health Check Comprehensive system health analysis and recommendations. - **Health Indicators**: Device connectivity, flow status, system performance - **Status Overview**: Connection status, system configuration, resource usage - **Recommendations**: Maintenance suggestions and optimization opportunities #### Zone Organization Guidance for organizing and optimizing zone structure. - **Zone Planning**: Logical grouping strategies for devices and areas - **Hierarchy Management**: Parent-child zone relationships - **Device Assignment**: Best practices for device-to-zone mapping ### Prompt Features - **Context-Aware**: All prompts analyze your current system state - **Real-Time Data**: Information is based on current device and system status - **Graceful Degradation**: Prompts work even when HomeyPro is temporarily unavailable - **Error Handling**: Clear error messages with suggested actions - **Actionable Guidance**: Practical steps you can take immediately ## Resource Caching The server provides intelligent resource caching with automatic fallback to stale data when HomeyPro is temporarily unavailable. ### Available Resources #### System Overview (`homey://system/overview`) Comprehensive system overview including device counts, zone counts, and health indicators. - **Content**: Device statistics, zone summary, system health percentage - **Cache TTL**: 5 minutes - **Use Case**: Dashboard displays, system monitoring, health checks #### Device Registry (`homey://devices/registry`) Complete device inventory with current states, capabilities, and online/offline indicators. - **Content**: Full device list with capabilities, states, and metadata - **Cache TTL**: 30 seconds (dynamic data) - **Use Case**: Device management interfaces, capability discovery, status monitoring #### Zone Hierarchy (`homey://zones/hierarchy`) Zone structure with device associations and parent-child relationships. - **Content**: Zone tree, device assignments, zone types and statistics - **Cache TTL**: 5 minutes - **Use Case**: Zone management, device organization, spatial automation #### Flow Catalog (`homey://flows/catalog`) Available flows with metadata, status, and execution statistics. - **Content**: Flow list with triggers, conditions, actions, and execution data - **Cache TTL**: 2 minutes - **Use Case**: Flow management, automation analysis, debugging ### Caching Features - **Intelligent TTL**: Different cache durations based on data volatility - **Stale Data Fallback**: Returns cached data when HomeyPro is unreachable - **Error Handling**: Graceful degradation with detailed error information - **Connection Resilience**: Continues operation during network issues - **Performance Optimization**: Reduces API calls and improves response times ### Cache Behavior 1. **Fresh Data**: Returns current data when cache is valid and HomeyPro is accessible 2. **Stale Data**: Returns cached data with staleness indicators when HomeyPro is unreachable 3. **Error Response**: Returns structured error information when no cached data is available 4. **Automatic Refresh**: Cache automatically refreshes when HomeyPro becomes available again ## API Tools The server provides comprehensive API tools for direct HomeyPro interaction. All tools support pagination and error handling with detailed responses. ### Device Tools #### Device Discovery and Information - **`list_devices`**: List all devices with pagination support - Optional compact mode for reduced data transfer - Excludes hidden devices automatically - Includes online/offline status for each device - **`get_device`**: Get detailed information about a specific device - Complete device details including capabilities and settings - Capability values and detailed configuration - Energy information and UI settings - **`get_devices_classes`**: List all available device classes - Useful for understanding device types before searching - Returns complete list of supported device categories - **`get_devices_capabilities`**: List all possible device capabilities - Comprehensive capability reference - Essential for understanding control options #### Device Search and Filtering - **`search_devices_by_name`**: Search devices by name with pagination - Fuzzy matching against device names - Includes note field information for context - Supports pagination for large result sets - **`search_devices_by_class`**: Search devices by class/type - Filter devices by specific categories (lights, sensors, etc.) - Paginated results with metadata #### Device Control and Monitoring - **`control_device`**: Control device capabilities - Set capability values (on/off, dimming, temperature, etc.) - JSON value parsing with fallback handling - Returns current device state after control - **`get_device_insights`**: Get historical device data - Multiple time resolutions (hour, day, week, month) - Custom timestamp ranges supported - Capability-specific insights and trends ### Zone Tools #### Zone Management - **`list_zones`**: List all zones with pagination - Complete zone hierarchy information - Parent-child relationships included - **`get_zone_devices`**: Get all devices in a specific zone - Zone-based device filtering - Compact mode option for performance - Online/offline status per device #### Zone Monitoring - **`get_zone_temp`**: Get average temperature for a zone - Automatically averages temperature sensors in the zone - Handles zones without temperature sensors gracefully ### Flow Tools #### Basic Flow Management - **`list_flows`**: List all standard flows with pagination - Complete flow metadata and configuration - Trigger, condition, and action information - **`trigger_flow`**: Execute a specific flow - Manual flow triggering - Success confirmation with flow details - **`get_flow_folders`**: Get all flow organization folders - Flow organization structure - Folder hierarchy for better management - **`get_flows_by_folder`**: Get flows in a specific folder - Folder-based flow filtering - Organizational flow management - **`get_flows_without_folder`**: Get unorganized flows - Find flows that need organization - Cleanup and maintenance assistance #### Advanced Flow Management - **`list_advanced_flows`**: List all advanced flows - Complex automation flows with scripting - Complete flow structure and metadata - **`get_advanced_flow`**: Get detailed advanced flow information - Full flow configuration and logic - Script content and execution details - **`trigger_advanced_flow`**: Execute advanced flows - Manual triggering of complex automations - Execution confirmation and status #### Advanced Flow Search and Filtering - **`search_advanced_flows`**: Search advanced flows by name/description - Text-based flow discovery - Paginated search results - **`get_enabled_advanced_flows`**: Get only enabled advanced flows - Active automation discovery - Performance and maintenance insights - **`get_disabled_advanced_flows`**: Get disabled advanced flows - Inactive flow identification - Maintenance and cleanup assistance - **`get_broken_advanced_flows`**: Get flows with errors - Error detection and troubleshooting - System health monitoring - **`get_advanced_flows_by_folder`**: Get advanced flows in folders - Organized advanced flow management - Folder-based filtering - **`get_advanced_flows_without_folder`**: Get unorganized advanced flows - Organization and cleanup assistance - Flow management optimization - **`get_advanced_flows_with_inline_scripts`**: Get flows containing scripts - Script-based automation identification - Advanced functionality discovery #### Flow State Management - **`enable_advanced_flow`**: Enable a disabled advanced flow - Flow activation with confirmation - State management and control - **`disable_advanced_flow`**: Disable an active advanced flow - Flow deactivation for maintenance - Temporary automation suspension ### System Tools #### System Information - **`get_system_info`**: Get comprehensive system overview - Connection status and system health - Device, zone, and flow counts - Online/offline device statistics - Flow status (enabled/disabled/broken) - System configuration (address, language, units) - Location coordinates and regional settings - Recommended as first call before other operations ### Tool Features #### Pagination Support - **Cursor-based pagination**: Efficient handling of large datasets - **Configurable page sizes**: Optimize for your use case - **Total count tracking**: Know the full dataset size - **Next page indicators**: Easy navigation through results #### Data Formats - **Compact mode**: Reduced data transfer for performance - **Full detail mode**: Complete information when needed - **JSON value handling**: Automatic parsing with fallbacks - **Error responses**: Structured error information with details #### Performance Optimization - **Hidden device filtering**: Automatic exclusion of system devices - **Efficient queries**: Optimized API calls to HomeyPro - **Connection reuse**: Persistent connections for better performance - **Graceful degradation**: Continues operation during partial failures #### Error Handling - **Detailed error messages**: Clear problem descriptions - **Connection status**: Network and API health indicators - **Fallback responses**: Graceful handling of API failures - **Logging integration**: Comprehensive error tracking ### Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Add tests if applicable 5. Submit a pull request ## License This project is licensed under the MIT License.

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5 months ago

Gimp Mcp Server

Maor

This project enables non-technical users to edit images with GIMP through simple conversational commands, bridging the gap between GIMP's powerful capabilities and natural language interaction. It also allows professionals to execute complex multi-step workflows faster than traditional point-and-click methods. Users can describe what they want to achieve - from basic photo adjustments to sophisticated artistic modifications. For example, "brighten the background and add a vintage filter" or "remove the red-eye and sharpen the subject" - and the system translates these requests into precise GIMP operations. The project is functional and exposes all GIMP features via MCP. The main development focus is creating comprehensive AI-readable documentation to help AI agents use GIMP efficiently.

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5 months ago

MCPX - MCP Gateway

main

Lunar.dev MCPX is a lightweight, centralized gateway for managing and securing your local-first MCP workflows. Designed for developers working with multiple MCP-compatible tools and services, MCPX acts as a smart proxy—routing requests, enforcing permissions, and simplifying integrations across your stack. At its core, MCPX helps you: * Dynamically route between multiple MCP servers * Connect to tools and services with zero code via simple JSON configuration * Expose a unified API interface across disparate services * Adopt a remote-first approach without losing the local flexibility MCP offers Whether you're exploring how to structure secure agent workflows or deploying agents into production environments, this documentation will walk you through every step—from setup to advanced configurations.

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5 months ago

mcp server for unix pty control

ianks

The native MCP server for Unix PTY control gives AI models authentic, low-latency terminal access by spawning true pseudo-terminals—not brittle AppleScript hacks—so you can open persistent Bash, Python, or SSH sessions, run commands like “ssh prod.server,” and stream only fresh output thanks to smart buffering. Built on Tokio for fully asynchronous, non-blocking I/O, the service supports multi-session workflows, letting developers switch between concurrent prod-debug shells without interruption while JSON-RPC endpoints such as tools/call create_session handle lifecycle automation. This combination of real PTYs, session persistence, and high-performance async design makes the MCP server an ideal foundation for DevOps automation, cloud debugging, and AI-driven terminal orchestration.

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5 months ago

Hal (http Api Layer)

Dean Ward

HAL is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models. It allows LLMs to make HTTP requests and interact with web APIs through a secure, controlled interface. HAL can also automatically generate tools from OpenAPI/Swagger specifications for seamless API integration. The system runs in an isolated environment with controlled access, ensuring security while maintaining fast performance through its TypeScript-optimized architecture. HAL offers comprehensive HTTP functionality including GET, POST, PUT, PATCH, DELETE, OPTIONS, and HEAD requests, enabling LLMs to fetch and send data to any HTTP endpoint. The platform features secure secret management through environment-based secrets with {secrets.key} substitution, automatic tool generation from Swagger/OpenAPI specifications, and built-in documentation that provides a self-documenting API reference. These capabilities make HAL a powerful bridge between language models and web-based services, facilitating seamless integration while maintaining security and performance standards.

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5 months ago