Model Context Protocol servers let AI agents reach beyond the codebase. They connect your agent to external APIs, databases, search engines, and services through a standardized protocol, so the agent can actually take action, not just write code.
1088 servers
An MCP server that standardizes and contextualizes Modbus data, enabling seamless integration of AI agents with industrial IoT systems. - Modbus Tools: - Read/write holding registers (readregister, writeregister). - Read/write coils (readcoils, writecoil). - Read input registers (readinputregisters). - Read multiple holding registers (readmultipleholdingregisters). - Prompt: Analyze Modbus registe
A Model Context Protocol (MCP) server implementation that integrates with Olostep for web scraping, content extraction, and search capabilities. To set up Olostep MCP Server, you need to have an API key. You can get the API key by signing up on the Olostep website. - Scrape website content in HTML, Markdown, JSON or Plain Text (with optional parsers) - Parser-based web search with structured resul
There is a notable gap in the current AI Agent ecosystem: - Most MCP Servers are simple wrappers around Web APIs - Functionality interfaces may not be complete, depending on developer implementation - Manual creation of MCP interfaces is time-consuming and error-prone - Lack of standardized conversion processes MCP Link solves these issues through automation and standardization, allowing any API t
A robust Model Context Protocol (MCP) server that provides secure shell command execution capabilities to AI assistants and other MCP clients. In other words: the brain thinks, this runs the commands. This tool creates a bridge between AI systems and your shell environment through the standardized MCP protocol. It exposes the system shell as a structured tool, enabling autonomous workflows, tool-a
A multi-model AI orchestration MCP server for automated code review and LLM-powered analysis. Multi-MCP integrates with Claude Code CLI to orchestrate multiple AI models (OpenAI GPT, Anthropic Claude, Google Gemini) for code quality checks, security analysis (OWASP Top 10), and multi-agent consensus. Built on the Model Context Protocol (MCP), this tool enables Python developers and DevOps teams to
A Model Context Protocol (MCP) server that exposes access to an InfluxDB v2 instance using the InfluxDB OSS API v2. Mostly built with Claude Code. This MCP server provides: - Resources: Access to organization, bucket, and measurement data - Tools: Write data, execute queries, and manage database objects - Prompts: Templates for common Flux queries and Line Protocol format The server exposes the fo
A personal filesystem that works as your life operating system — designed to be read, queried, and operated on by an AI agent. Life OS is a structured set of markdown files that give an AI agent (Goose, Claude, or any LLM with filesystem access via MCP) persistent context about your life — who you are, what you're working on, what you're learning, and what's happened. The AI reads these files at t
TL;DR for AI agents — enquire-mcp is the 1 Obsidian MCP: a local memory and document-intelligence layer that makes one vault persistently searchable from Claude Code, Claude Desktop, Cursor, ChatGPT, Codex, OpenClaw, and any MCP client. It returns cited source context across Markdown, PDFs + OCR, Canva
Piston MCP Server piston-mcp is an MCP server that allows LLMs to connect to and execute code using Piston . You can try out piston-mcp locally without cloning it. To try out piston-mcp you'll need to install uv: You will also need to download an MCP client to connect to piston-mcp, such as Claude Desktop. Update the MCP client's configuration with the following configuration to connect to piston-
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.
This MCP Server is used to summarize your chat messages. - querychatmessages - Query chat messages - Query chat messages with given parameters - Summarize chat messages based on the query prompt 1. Set up environment variables: create .env file in the root directory, and set your chat database path. 2. Install dependencies: Build the server: For development with auto-rebuild: To use with Claude De
This project provides an MCP (Model Context Protocol) server for interacting with Kaggle from Claude Desktop (or any MCP-compatible client) pip install -r requirements.txt Go to your Kaggle account -> Settings Scroll to API section → click Create New API Token. This will download a file called kaggle.json. Place kaggle.json in the following location on your system: ~/.kaggle/kaggle.json C:\Users\
A Rust MCP server that gives Claude (Code, Desktop, or web) read/write access to your Workflowy graph, plus a generic template for turning that raw access into a working second brain. There are two ways to use it: 1. Bare MCP server. Wire the binary into your MCP host and call the 26 tools directly. No templates, no opinions. 2. Second brain. Hand BOOTSTRAP.md to Claude. The assistant follows the
A MCP server to connect to wolfram alpha API. This is analogous to the !wa bang in duckduckgo search. Query Wolfram Alpha api. You must set the WOLFRAMAPIKEY environment variable. Get an api ket from Wolfram Alpha. This was tested with the full results API, but it might not be required. Since the official MCP inspector does not have good environment support, I reccommend using wong2's mcp-cli-insp
Hardware-accelerated persistent memory for AI agents. Local-first. No cloud. One-time payment. 66.9% on LoCoMo benchmark (adjusted). Under 1ms retrieval. Zero cloud dependency. Retrieval pipeline rebuilt from scratch. - bge-small-en-v1.5 bi-encoder + ms-marco cross-encoder reranker (spec-decode architecture) - BM25 + Porter-stemmed BM25 + named entity injection, fused via RRF - MAGMA graph layer —
A Model Context Protocol (MCP) server for Microsoft Bing Search API integration, allowing AI assistants to perform web, news, and image searches. - Web search for general information - News search for recent events and timely information - Image search for visual content - Rate limiting to prevent API abuse - Comprehensive error handling - Python 3.10 or higher - Microsoft Bing Search API key - MC
An unofficial Model Context Protocol (MCP) server for interacting with PayPal Braintree payment processing services. This project is available under the MIT License with an Academic Citation Requirement. This means you can freely use, modify, and distribute the code, but any academic or scientific publication that uses this software must provide appropriate attribution. If you use this software in
Cross-platform MCP (Model Context Protocol) server exposing LLM-accessible tools to list and kill OS processes via natural language queries. Perfect for shy ninjas who just want rogue processes gone: "Find and nuke the damn CPU glutton choking my system!" The following tools are exposed to MCP clients: - processlist: Lists running processes sorted by CPU or memory with optional name, user, status,
A powerful Model Context Protocol (MCP) server designed for advanced video and audio editing operations. This server enables MCP clients—such as Claude Desktop, Cursor, and others—to perform comprehensive multimedia editing tasks through a standardized and unified interface. - Basic Editing: Trim, merge, resize, crop, rotate videos - Effects: Speed control, fade in/out, grayscale, mirror - Overlay
Claude Code forgets everything between sessions. CogmemAi fixes that. One command. Claude Code remembers your architecture, patterns, decisions, bugs, and preferences — permanently. That's it. The setup wizard verifies your API key, configures Claude Code, and you're ready. Start Claude Code by typing claude and your memories are ready. Don't have an API key yet? Get one free at hifriendbot.com/de
DevDocs-MCP is a Model Context Protocol (MCP) server that provides version-pinned, deterministic documentation sourced from DevDocs.io to AI assistants (Claude, RooCode, Cline, Copilot etc.). It acts as a local Documentation Intelligence Layer, ensuring your agent always has the correct API context without network latency or training data drift. This server follows the proposed MCP server standard
An MCP (Model Context Protocol) server that lets Claude, ChatGPT, and other LLM agents search and discover APIs using natural language via Semantic API. Ask for any API capability in plain English and get back endpoint details, parameters, auth info, and code snippets. Or run directly with uvx: Sign up at semanticapi.dev to get your API key. Add to your Claude Desktop config (~/Library/Application
This repo contains the source code for running a local MCP server that interacts with APIs for Google Analytics. Join the discussion and ask questions in the 🤖-analytics-mcp channel on Discord. The server uses the Google Analytics Admin API Google Analytics Data API to provide several Tools for use with LLMs. - getaccountsummaries: Retrieves information about the user's Google Analytics accounts
An MCP (Model Context Protocol) server that enables running Bruno collections. This server allows LLMs to execute API tests using Bruno and get detailed results through a standardized interface. Run Bruno collections using the Bruno CLI Support for environment files Support for environment variables Detailed test results including: Overall success/failure status Test summary (total, passed, failed