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

Notes-centric MCP offering a custom note:// scheme, summarization prompt, and add-note tool with Claude integration and client notifications.

Experimental Claude integration with desktop tooling enabling seamless prompt chaining and local context sharing.

High-performance USDA data access via Claude Desktop with fast SQLite, advanced search, nutrient density scoring, and caching.

OpenAPI-driven bridge translating API specs into a unified, context-aware tool interface with protocol-compatible interoperability.

Weather context provider with pluggable data sources and real-time updates for modular AI workflows.

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.

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.

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

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

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

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.

Lightweight Python-based context protocol implementation with modular hooks and minimal footprint.

Python-based MCP 2.5J server enabling secure, modular context exchange with pluggable adapters and tooling integration.

Lightweight Python framework enabling modular context sharing and orchestration across AI agents.

Python-based, modular context management with extensible workflows and clean API design for scalable AI integrations.