Browse the catalog

A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, allowing users to query work items with plans to support creating/updating items, managing pipelines, handling pull requests, and administering sprints and branch policies.

AivisSpeech MCP Server provides a bridge between AI assistants and text-to-speech capabilities through the AivisSpeech API. This implementation enables AI agents to generate and play speech audio from text input, with configurable speaker settings. The server exposes the speech synthesis functionality as MCP tools that can be called programmatically, making it valuable for applications requiring voice output such as accessibility tools, language learning, or interactive voice assistants.

This extension allows Claude to interact with Wavebox, enabling tab management, navigation, page content reading, and browser automation. It uses Wavebox's AppleScript API to control the browser programmatically.

Official MCP (Model Context Protocol) server for FalconFeeds.io

Provides AI assistants with instant access to comprehensive Medusa.js v2 documentation, enabling smart search, precise section retrieval, and complete browsing through 2,105+ documentation sections.

Provides weather information for any city through the Model Context Protocol.

MCP server for SMS.RU — SMS sending, delivery status, balance (Russia)

MCP TabNews provides access to TabNews content through a set of tools that interact with the TabNews API. Developed by Renan Teixeira, this TypeScript implementation offers capabilities for retrieving content listings, user posts, comments, and analytics data from TabNews. The server exposes nine tools including content retrieval, user content filtering, comment access, and analytics reporting functions. It's particularly useful for AI assistants that need to search, analyze, or reference TabNews discussions without direct API integration, and can be deployed via Smithery or run locally with Node.js.

A demonstration MCP server implementation with Docker support that provides a simple hello-world tool and includes a web-based inspector for interactive testing and exploration of MCP tools.

Enables AI agents to fundraise for projects on the Base chain using milestone-based escrow management. It allows agents to create proposals, track funding progress, and generate payment release requests upon completing work.

Gong MCP — wraps the Gong API v2 (OAuth)

A Model Context Protocol server that provides clj-kondo linting capabilities for Clojure/ClojureScript/EDN files, useful for environments like Claude code and desktop that lack built-in linting.

Provides scored and reasoned Kyoto temple data optimized for AI travel agents to deliver personalized recommendations based on natural language queries. It enables agents to retrieve detailed temple information including historical significance, pricing, and crowd levels.

Detects a company CRM, sequencer, and marketing automation from its public website. Clay-ready.

Helps LLMs choose the best AI model for each coding agent by fetching real-time data from OpenCode subscriptions and matching with benchmarks and reasoning capabilities from OpenRouter.

Enables AI assistants to query and analyze Google BigQuery data, including schema browsing, running queries, and comparing datasets through natural language.

MCP Server Sentry provides a bridge between AI models and the Sentry error tracking service, allowing models to query and analyze error reports programmatically. Built with TypeScript, it offers tools for retrieving detailed issue information including stack traces, event counts, and timestamps through a clean API interface. The implementation handles URL parsing to extract organization and project information, making it ideal for developers seeking to incorporate error analysis capabilities into AI-assisted debugging workflows or automated error triage systems.

Web search MCP server for AI agents — DuckDuckGo + content extraction

MCP server for valet parking: 789 US operators across 31,186 cities. 7 tools. No auth.

This MCP server implementation provides a bridge to Apple Notes, enabling AI assistants to interact with and manage notes. Developed by Rafal Wilinski, it offers tools for listing, searching, creating, and indexing notes using both semantic and full-text search capabilities. The server utilizes LanceDB for efficient data storage and retrieval, and integrates with the Hugging Face Transformers library for on-device embeddings. It's designed for use cases requiring natural language interactions with Apple Notes, such as note organization, content retrieval, and automated note creation.