Protocol-level CLI enabling dynamic tool exploration across OpenAI and Ollama with interactive chat and multi-model workflows.

Hacking on some MCP clients

Lightweight tooling to manage and optimize model context lifecycles, enabling clean prompts, context binding, and workflow automation.

Starter scaffolding for model context protocol integration, emphasizing a clean, extensible structure for rapid developer onboarding.

Lightweight, extensible client that integrates context into apps, enabling secure sharing, prompt orchestration, and modular, plugin-driven workflows.

Cross-platform MCP command executor with shell and direct modes, configurable working directory, and robust error reporting.

Go-native, high-concurrency context delivery with pluggable transports and strong typing for seamless integration.

Lightweight JavaScript demonstration showcasing core context sharing concepts with modular components and a minimal API surface.

Launches MCP Inspector by generating a macOS app from a start script; requires Node.js and opens in Terminal.

Lightweight Claude MCP server with modular prompts and robust context handling for seamless integration.

No public README content available to determine distinctive features or usage of mcp2.5.

Lightweight, versioned AI context broker enabling modular agents to share intents and context with robust serialization and extensible codecs.

Python-based context-driven orchestration with modular plugins and secure routing for flexible AI tool integration.

Python-based, modular implementation enabling structured model context exchange with extensible plugins and lightweight setup.

Lightweight Python implementation featuring modular context handling, pluggable sources, and streamlined integration for diverse, ecosystem-friendly agent workflows.

Repository-focused context tools enable file listing, content retrieval, structural navigation, and semantic search to empower precise code reasoning.

Minecraft Server

Lightweight Python server delivering efficient context routing and safe multi-agent state sharing with extensible adapters.

A modular Python server implementing MCP 2.0.5 for scalable, cross-model context management and orchestration.

Lightweight Python toolkit enabling flexible context management with pluggable backends for multi-tenant AI assistants and seamless tool integration.

Low-latency voice-activated assistant streaming microphone input to OpenAI and enabling local tool calls through a bridge.

This project implements an operating system's Master Control Program (MCP) that manages multiple processes using scheduling, signals, and process control. MCP launches processes, monitors their resource usage, and schedules them using time-slicing, demonstrating core OS concepts like multiprocessing and resource management.

Lightweight, extensible runtime for dynamic context management and tool orchestration across AI workflows.

Python-based MCP 2.0.5 with minimal docs; feature details and integrations are currently unspecified.