Enables LLMs to interact with the AT Protocol ecosystem, including Bluesky, through natural language. Supports public data access without authentication and full write operations with authentication.
Enables AI assistants to access real-time web data through search, markdown scraping, and browser automation while bypassing anti-bot protections. It provides tools for web research, e-commerce monitoring, and data extraction from across the globe.
Enables an MCP client to read Telegram through your own account — searching and reading chats, folders, forum topics, threads, albums, reactions, and polls, plus discovering public channels — without sending messages or marking anything read. It also supports durable change checkpoints, named watch subscriptions, and source-linked JSONL/Markdown history exports.
Unified MCP orchestration layer that consolidates multiple MCPs into a single interface with semantic tool discovery, code-mode execution, scheduling, and intelligent caching to reduce token usage by 97% and eliminate choice paralysis.
An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.
A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.
Enables AI agents to access and utilize GovBR Design System documentation, including component details, accessibility guidance, and search functionality via Model Context Protocol.
Provides a Model Context Protocol server with tools for knowledge base search, retrieval, and addition, plus system time, resources, and prompts. Supports stdio and Streamable HTTP transports for local and shared deployments.
A bridge between AI assistants and the Paradex perpetual futures trading platform for managing accounts, vaults, and orders. It enables users to retrieve real-time market data, monitor positions, and execute trading strategies through the Model Context Protocol.
A production-grade TypeScript starter for building Model Context Protocol servers, supporting stdio and Streamable HTTP transports with modular tools, resources, and prompts.
A smart AniList integration for the Model Context Protocol that provides AI assistants with tools for searching media, managing watchlists, and analyzing user anime or manga tastes. It goes beyond basic API calls by offering personalized recommendations, taste comparisons, and natural language profile summaries.
A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.
DevEyes is a Model Context Protocol (MCP) server that captures screenshots from your local development environment and automatically optimizes them for LLM consumption.
A Model Context Protocol server that exposes a MeshCore node and the mesh reachable through it as a clean, high-signal interface for AI agents or tools.
An MCP server that demonstrates the Model Context Protocol using a mock SQLite database of an online store, offering tools for product search, sales analysis, and reviews, along with resources and prompts for learning MCP integration.
Reverse engineering engine built for AI agents, written in Rust from scratch. Turns binaries into readable code checked against the machine, with the analysis an agent needs around it, fast and sized for the context window.
Gives AI assistants and research code read-only, normalized access to trusted humanitarian datasets — 75 years of UNHCR displacement statistics, World Bank indicators, HDX crisis data on conflict, food security, funding and internal displacement, and ReliefWeb situation reports — through the Model Context Protocol. Unifies country codes to ISO3 with fuzzy English and Arabic name resolution, applies each dataset's correct aggregation semantics, and attaches a citable source and reproducible extraction manifest to every payload.
