Modular platform enabling context-aware AI agents via hubgenial and xgenial integration.

macOS-ready MCP GitHub integration with tested config, token validation, and API connectivity.

OpenMCPSever offers a modular, open-source protocol toolkit with plugins, focused on extensibility, performance, and seamless client integration.

Claude-centric context routing with modular plugins and secure, auditable data flows for Claude-powered workflows.

Go-based MCP server with concurrency-first processing, modular plugins, and easy integration.

Go-based, lightweight implementation for managing model context with extensible providers and JSON-RPC-style messaging.

A lightweight TypeScript implementation offering note resources with note:// URIs, a create_note tool, and summarize_notes prompts for structured summaries.

Bridges AI-driven inputs to games via a portable WebSocket conduit, enabling seamless cross-tool interaction and automated input workflows.

Node.js proof-of-concept implementing MCP basics to prototype context sharing and lightweight routing.

Unified manager for MCP servers enabling one-line add/remove, web UI, and flexible environment and path customization to streamline deployments.

Lightweight framework for orchestrating contextual workflows with extensible plugins and smooth integration across tools and models.

Diverse toolkit delivering data access, automation, storage, and repository integrations across local and cloud environments.

Lightweight Python client enabling seamless MCP-to-GitHub integration with token-based auth, clear setup, and practical usage examples.

Automates scaffold and launch of a specialized inspector tool, delivering a ready macOS app in ~/Applications with Terminal-based execution.

Claude-based context routing and integration scaffolding with extensible adapters for seamless AI workflow orchestration.

Connecting Twitter trends with Claude analytics to reveal actionable business opportunities.

Lightweight, extensible context management system with pluggable backends, isolation, and developer-friendly APIs.

Context-aware orchestration for language models with modular plugins and dynamic task routing.

Free Java client to test MCP API with quick experiments and extensibility.

Kotlin-based framework for Week9 Model Context Protocol experiments, featuring modular architecture and extension-friendly design for rapid experimentation.

Modular context orchestration with plugin-driven tool integration and adaptive memory for secure, dynamic AI workflows.

Lightweight, Python-based approach offering robust context handling, modular extensibility, and protocol-driven model orchestration for adaptable AI workflows across varied deployments.

Lightweight, context-first runtime enabling seamless model interactions and extensibility with minimal dependencies.

Python-based core enabling dynamic model-context sharing, multi-agent collaboration, and plugin-driven extensions with lightweight deployment.