— · MIT
MCP Server Framework and Tool Development library for building custom capabilities into agents.
— · MIT
Harness the power of local LLMs with this TUI MCP Client for Ollama. Featuring all core MCP primitives (tools, prompts, resources), agent mode, multi-server, model switching, streaming responses, human-in-the-loop, thinking mode, model params config, system prompts, and saved preferences.
— · Apache-2.0
Local code intelligence MCP server and CLI for AI coding agents
— · no license
Enterprise AI Platform with guardrails, MCP registry, gateway & orchestrator
— · MIT
Elegantly manage your MCP services
— · no license
Run MATLAB® using AI applications with the official MATLAB MCP Server from MathWorks®. This MCP server for MATLAB supports a wide range of coding agents like Claude Code® and Visual Studio® Code.
— · MIT
MCP Toolkit for Flutter AI Agent Driven Development (MCP/CLI + custom client side tools) - via closed feedback loop (visual & semantic snapshot) and high client side customization adaptable for any Flutter app. Nowadays it is often called as agentic harness.
— · AGPL-3.0
AI-assisted PCB design for KiCAD 10. Native KiCAD plugin — a single Rust binary exposing 171 schematic, layout, routing, design-review, and manufacturing tools to Claude, or the LLM of your choosing
v1.13.0 · PolyForm-Noncommercial-1.0.0
Lossless, project-scoped memory for AI coding tools. Durable context across sessions with MCP tools, FTS5 search, cloud sync, trace optimization, Claude/Codex/OpenCode/Gemini wrappers, skill packs, and automatic hooks.
— · Apache-2.0
Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth, Observability, Debugger, Telemetry & Runtime • Run real-world MCPs powering AI Agents
v1.2.0 · no license
🌋 Build AI agents that seamlessly combine LLM reasoning with real-world actions via MCP tools — in just a few lines of TypeScript.
— · MIT
CTX: a tool that solves the context management gap when working with LLMs like ChatGPT or Claude. It helps developers organize and automatically collect information from their codebase into structured documents that can be easily shared with AI assistants.