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: A secure, **multi-tenant** Python MCP server framework built to integrate easily with external services via OAuth 2.1, offering scalable and robust solutions for managing complex AI applications.

- Create a new MCP server in TypeScript, batteries included - supports user-defined templates!

- Fast and elegant Typescript framework for building MCP servers

- Anthropic's Model Context Protocol implementation for Oat++

- A TypeScript framework for building MCP servers elegantly

- A lightweight utility designed to simplify the deployment and management of MCP servers, ensuring ease of use, consistency, and security through containerization

- AI Agents framework with 64+ built-in tools for search, memory, workflows, code execution, and file operations. Turn any AI assistant into a multi-agent system with MCP.

📇 - A TypeScript framework for building production-ready MCP servers with automatic tool discovery, multi-transport support (stdio/SSE/HTTP), built-in validation, and zero-config setup.

- When tool count exceeds LLM context limits, accuracy collapses (248 tools → 12%). graph-tool-call builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy to 82% with 79% fewer tokens. Zero dependencie

📇 - MCP server that exposes RocketRide AI pipelines as tools for Claude, Cursor, and Windsurf. Self-hosted, open-source pipeline tool with multi-LLM support.

- Durable, agent-native AI runtime with native MCP client + server and A2A support. Rust core for performance, Python authoring for ergonomics. Features graph-based workflows, durable execution, and multi-agent coordination.

- A high-level framework for building MCP servers in Python

📇 🪟 - Filesystem-direct Obsidian MCP server with low context-tax. Reads your vault directly from disk — no Local REST API plugin required. ~575× smaller payloads than the REST plugin. 8 tools. (also: )

🪟 - Japan-specific utilities for AI agents: era ↔ Western year conversion (令和8年 ↔ 2026), kanji-to-romaji transliteration, 7-digit postal code lookup, national holiday calendar, hiragana ↔ katakana conversion, full-width ↔ half-width normalization, and statistical Japanese name splitting. 9 tools, MI

📇 - Source-available, self-hosted AI workflow automation platform. Build multi-agent, RAG, and tool-using pipelines on a visual canvas, then expose any workflow as an MCP server (stdio/SSE/Streamable HTTP), or call external MCP servers from the agent node.

🪟 - Minimal Go wrapper on the official modelcontextprotocol/go-sdk for stdio MCP servers: struct-tag JSON Schema inference, library, reference binary , ~5MB static builds. For teams that want go-sdk compliance with minimal boilerplate ().

- TurboMCP SDK: Enterprise MCP SDK in Rust

- File-based Coordination Protocol: behavior governance layer for multi-agent teams. 45 MCP tools (write_task, write_report, write_issue, write_review, lifecycle claim/submit/approve…). Agents coordinate via structured Markdown files (\_lifecycle/\) — no message broker, no database, just the filesys

- AI Agent Mission Control with 200+ MCP tools. Manage agents, experiments, workflows, crews, skills, and more via stdio + HTTP/SSE. Self-hosted, open-source (AGPL-3.0). Remote server:

- A Gin-like framework for building MCP servers in Go. Struct-tag auto schema, middleware chain, auth, tool groups, adapters for Gin/OpenAPI/gRPC, async tasks, Inspector UI.