Modular MCP 2.5 server enabling scalable context management and tool orchestration for autonomous agents.

MCP2.5

A scalable Python-based platform for cross-model context sharing and dynamic prompt optimization across heterogeneous AI models.

Java-based MCP server that imports GPT-derived context into a unified model context workflow, enabling seamless cross-model sharing.

MCP 2.0.5

mcpnuevo

Lightweight Python module for dynamic context handling and model interactions via modular adapters and efficient runtime.

Lightweight Unity C# implementation to receive SMF mocopi UDP motion data without binary libraries.

MCPPDLL

mcp 2.0

Lightweight Python implementation offering modular plugins, efficient context handling, and extensible tooling for seamless AI workflow orchestration.

Python-based tool that discovers, catalogs, and exports context endpoints with version awareness for automated workflows.

Java-based tool to assemble blocks for Model Context Protocol workflows, enabling structured, reusable context construction.

Python toolkit to streamline MCP integrations with reusable context blocks, adapters, and testing utilities for reliable, scalable deployments.

MCPROJECT

Audio MCP Server

A Python development toolbox offering file operations, code analysis, virtual environment management, and safe code execution via a unified interface.

Mirror of https://github.com/TimHolden/figma-mcp-server

MCP Client implementation for Gemini models. Reference: https://modelcontextprotocol.io

This project provides a **Model Context Protocol (MCP)** server that enables interaction with a Kafka cluster via standard tool-based interfaces. It exposes Kafka operations like producing messages, consuming, listing topics, and more — all accessible through Claude Desktop or any MCP-compatible client.

Geolocation, routing, and navigation powered by NextBillion.ai APIs with an extensible integration framework.

Multi-database PostgreSQL adapter with connection pooling, auto-reload, secure credentials, and admin tooling for dynamic environments.

A context-aware MCP server that centralizes, indexes, and lazily loads rules from multiple sources to deliver project-specific guidance with minimal context overhead.

Real-time context streaming powered by SSE with FastAPI, docker-ready deployment, and simple startup.