Mcp AI Agent Skills

Fast, token-efficient web content extraction for AI agents - converts websites to clean Markdown. Existing MCP web crawlers are slow and consume large quantities of tokens. This pauses the development process and provides incomplete results as LLMs need to parse whole web pages. This MCP package fetches web pages locally, strips noise, and converts content to clean Markdown while preserving links.

A Model Context Protocol Server for Home Assistant. See Model Context Protocol for context. The custom component required by this integration is moving to Home Assistant Core in https://github.com/home-assistant/core/pull/134122 1. Install the home-assistant-model-context-protocol custom component in your Home Assistant instance 1. Create a Long Lived Access Token 1. Clone this git repo to a path

RAGMap is a lightweight MCP Registry-compatible subregistry + MCP server focused on RAG-related MCP servers. - Ingests the official MCP Registry, enriches records for RAG use-cases, and serves a subregistry API. - Exposes an MCP server (remote Streamable HTTP + local stdio) so agents can search/filter RAG MCP servers. MapRag is a discovery + routing layer for retrieval. It helps agents and humans

An MCP server that provides safe access to your iMessage database through Model Context Protocol (MCP). This server is built with the FastMCP framework and the imessagedb library, enabling LLMs to query and analyze iMessage conversations with proper phone number validation and attachment handling. - macOS (required for iMessage database access) - Python 3.6+ Install all required dependencies: - fa

PiloTY (PTY for your AI Copilot) is an MCP server that gives an agent a persistent, interactive terminal. If you have used Claude Code / Codex to run shell commands, you have probably hit the same wall: tool calls tend to be stateless. Each call starts "fresh", so environment variables disappear, interactive programs cannot be driven reliably, and long-running processes get cut off or orphaned whi

Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Also handles MCP setup and configuration.

Universal Deployment Tracker for AI Assistants Track deployments across all platforms directly in your AI conversation. No more context-switching. No more dashboard hunting. Watch Full Demo · Live Badge Demo ───────────────────────── Get started in under 30 seconds: That's it! The server is now running and ready to be configured in your AI assistant. ───────────────────────── deploy-mcp supports m

The MCPR (Model Context Protocol Tools for R) package addresses a fundamental limitation in the current paradigm of AI-assisted R programming. Existing AI agents operate in a stateless execution model, invoking Rscript for each command, which is antithetical to the iterative, state-dependent nature of serious data analysis. An analytical workflow is a cumulative process of exploration, modelling,

This is a Model Context Protocol (MCP) server designed specifically for AI agents and LLM applications. It acts as a bridge between artificial intelligence systems and the comprehensive OpenStreetMap tagging knowledge base provided by the official @openstreetmap/id-tagging-schema library. Current Status: Production-ready MCP server, actively maintained and continuously improved. The service is dep

This is the MCP server of atest. Please start the MCP server with atest gRPC port. or start in Docker or start in npx You can also set the MCP server mode with the --mode flag. or as stdio mode: You can build the binary with the following command: You can build .dxt package with the following command: You can publish the package with the following command:

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. - Vector-based documentation search and retrieval - Support for multiple documentation sources - Semantic search capabilities - Automated documentation processing - Real-time context augmentation f

A Model Context Protocol (MCP) server that loads and serves multiple OpenAPI specifications to enable LLM-powered IDE integrations. This server acts as a bridge between your OpenAPI specifications and LLM-powered development tools like Cursor and other code editors. - Loads multiple OpenAPI specifications from a directory - Exposes API operations and schemas through MCP protocol - Enables LLMs to

An MCP (Model Context Protocol) server that provides unified access to multiple Large Language Model APIs for AI coding environments like Cursor and Claude Desktop. - 🌐 8 LLM Providers – ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral - 🎯 Smart Model Selection – Tag-based preferences (coding, business, reasoning, math, creative, general) - 📊 Prompt Logging – Track all prompts

A full-featured Model Context Protocol (MCP) server that provides seamless access to CustomGPT.ai APIs. Interact with your CustomGPT agents directly through Claude Code, Claude Web, and any MCP-compatible client. - List Agents: Browse all your CustomGPT agents with pagination - Get Agent Details: Retrieve detailed information about specific agents - Create Agents: Create new agents from sitemaps o

A Model Context Protocol (MCP) server for integrating with the Tomba.io API. This server provides comprehensive email discovery, verification, and enrichment capabilities through a standardized MCP interface. - Domain Search: Find all email addresses associated with a domain - Email Finder: Generate likely email addresses from names and domains - Email Verifier: Verify email deliverability and che

A Golang-based Model Context Protocol (MCP) server implementation for Prometheus that enables natural language interactions with Prometheus metrics and queries. Built with Go, prometheus-mcp-server provides a robust, type-safe interface that maintains full consistency with the Prometheus HTTP API, allowing you to query and manage your Prometheus instance through natural language conversations with

A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously. - Parallel Searching: Supports searching with multiple keywords on Google simultaneously, improving search efficiency - Browser Optimization: Opens multiple tabs in a single browser instance for efficient parallel searching - Automatic Verification Handling: Intelligently detects CAPTCHA

Fast, local-first domain availability checks for MCP clients. Works with zero configuration using public RDAP/WHOIS, and optionally enriches results with registrar pricing via a backend you control. 🆕 v1.10.0: GoDaddy public endpoint integration! Enhanced fallback chain (RDAP → GoDaddy → WHOIS) with premium/auction domain detection. Circuit breaker pattern ensures resilience. 🤖 v1.9.0+: AI-power

A Model Context Protocol (MCP) server that provides on-demand access to DexPaprika's cryptocurrency and DEX data API. Built specifically for AI assistants like Claude to programmatically fetch real-time token, pool, and DEX data with zero configuration. DexPaprika MCP connects Claude to live DEX data across multiple blockchains. No API keys required. Installation | Configuration | API Reference Ne

mcp.apify.com The Apify Model Context Protocol (MCP) server at mcp.apify.com enables your AI agents to extract data from social media, search engines, maps, e-commerce sites, and any other website using thousands of ready-made scrapers, crawlers, and automation tools from Apify Store. It supports OAuth, allowing you to connect from clients like Claude.ai or Visual Studio Code using just the URL.

Open-source memory backend for multi-agent systems. Agents store decisions, share causal knowledge graphs, and retrieve context in 5ms — without cloud lock-in or API costs. Works with LangGraph · CrewAI · AutoGen · any HTTP client · Claude Desktop Key capabilities for agent pipelines: - Framework-agnostic REST API — 15 endpoints, no MCP client library needed - Knowledge graph — agents share causal

An MCP Server to utilize Codelogic's rich software dependency data in your AI programming assistant. The server implements five tools: - codelogic-method-impact: Pulls an impact assessment from the CodeLogic server's APIs for your code. - Takes the given "method" that you're working on and its associated "class". - codelogic-database-impact: Analyzes impacts between code and database entities. - T

iptables for MCP. Blocks dangerous tool calls, scans for secret leakage, logs everything. No AI, no cloud, pure rules. Sits between your AI coding tool (Claude Code, Cursor, Windsurf) and MCP servers, intercepting every JSON-RPC message and enforcing YAML-defined policies. MCP servers have full access to your filesystem, shell, databases, and APIs. When an AI agent calls tools/call, the server exe

Generate Apache ECharts with AI MCP dynamically for chart generation and data analysis. Also you can use mcp-server-chart to generate chart, graph, map. - Fully support all features and syntax of ECharts, include data, style, theme and so on. - Support exporting to png, svg, and option formats, with validation for ECharts to facilitate the model's multi-round output of correct syntax and graphics.