Mirror of https://github.com/kaichen/mcp-config-editor

Mirror of https://github.com/spullara/mcp-server

Mirror of https://github.com/kazuph/mcp-pocket

Mirror of https://github.com/mzxrai/mcp-webresearch

Mirror of https://github.com/varunneal/spotify-mcp

A lightweight, multi-format content fetcher enabling on-demand HTML, JSON, TXT, and Markdown outputs with format conversion.

Self-hosted MCP server enabling LLMs to securely access local Home Assistant data via token-based MCP, with Smithery and manual setup.

Unifies LinkedIn feeds and job data via an unofficial API for programmable, seamless access.

A modular sports data engine focused on stats, live scores, fantasy data, and betting insights for apps.

Downloads and parses Substack posts, extracting title, author, subtitle, and content, with Claude AI desktop integration.

Integrates Google Tasks with a modular backend, offering search, list, create, update, delete, and local OAuth credential flow.

Claude-driven X posting: create, manage, and publish tweets and threads directly via the X API.

A Model Context Protocol adapter that fetches YouTube captions with yt-dlp and feeds them to Claude for concise, context-rich summaries.

Interact with Tinybird workspaces via MCP to query data sources, run endpoints, and push datafiles with Claude integration.

Downloads YouTube subtitles with yt-dlp and connects to Claude via Model Context Protocol for quick AI-assisted video insights.

Read-only MCP server delivering Bybit market data and instrument details via MCP for safe, model-driven queries.

A bridge between YouTube API and AI assistants enabling caption download and video summary generation.

Modular, plugin-driven Python toolkit to orchestrate context-aware workflows with clean integration and scalable inter-component communication.

Notion API integration for Claude to read, update, and manage Notion workspaces with rich block and database operations.

Generates customizable context environments with automated file creation, tool management, and robust debugging for rapid development workflows.

Rust-based, learning-focused Model Context Protocol implementation for hands-on protocol exploration and experimentation.