# Context Management

This project is an MCP service based on Apache Unomi, enabling Claude to maintain context through user profile management, providing functions such as profile lookup, attribute management, and session handling.

An MCP server that provides intelligent text summarization functions for AI agents, optimizing context window management and improving AI work efficiency

MCP Remote Context Server for Copilot Integration

A Node.js-based Cardano blockchain integration service that provides tool registration, knowledge base management, and context processing functions.

The MCP Cobot Server is an implementation of the server based on FastMCP, designed specifically for the M5 MyCobot280 collaborative robot, implementing the MCP standard for efficient context management.

This project implements the Model Context Protocol (MCP) framework for dynamic management of query routing and execution context, supporting climate data analysis.

MCP is an open protocol for standardizing the context of AI applications, similar to the USB - C interface in the AI field, enabling seamless connection between LLMs and various data sources/tools. It provides preset integrations, multi - model switching, secure data processing, and a standardized interface, supporting the construction of LLM - based intelligent agents and complex workflows.

The MCP Protocol SDK implemented in TypeScript for building standardized services interacting with LLMs

Database Model Context Protocol (DB-MCP) Server

The MCP Context Template is a repository for centrally managing AI tool contexts, aiming to reduce the cost of context sharing and improve development efficiency, providing a unified AI collaboration standard and document structure for teams.

A model context protocol SDK implemented in TypeScript, providing client - side and server - side functions for integration with LLMs.

Brainrot MCP is a model context protocol server that solves the problem of context loss in AI programming conversations. By storing project decisions, to - do lists, and architectural patterns, it enables persistent memory and intelligent retrieval across conversations.

A simple MCP server prototype based on Node.js and Express for interacting with the OpenAI API through structured contexts, supporting dynamic character updates and story plot generation.

A model context protocol (MCP) SDK implemented in Python for building standard servers and clients interacting with LLMs

Popmelt MCP is a framework that enables in - depth personalized AI interactions through multi - dimensional user preferences, including core components such as style vectors, context weighting, and a dynamic design system.

The Model Context Protocol (MCP) SDK implemented in TypeScript, providing client and server functions for standardizing LLM context interactions.

A context management and caching MCP server based on Gemini, supporting a large context window of 2M tokens and providing session management and API caching functions.

This project demonstrates how to integrate Claude AI into the development process through the MCP protocol, including an MCP server and a to-do application demonstration to achieve AI-assisted software development.

Model Context Protocol Server Project

The HuLa MCP Service is a TypeScript implementation based on the Model Context Protocol, providing a backend service for the HuLa instant messaging application with functions such as resource access, tool operations, and context management.

An MCP server for extending the context window/memory of AI agents, supporting the storage, retrieval, and management of memory content, including semantic search functionality.

A model context protocol SDK implemented in TypeScript, supporting client and server functions for integration with LLMs.

Memory Bank MCP is a server based on the Model Context Protocol (MCP) for managing Memory Banks, allowing AI assistants to store and retrieve information across sessions. It supports remote SSH server storage, provides multiple operating modes and tools to help AI assistants maintain context and track cross - session progress.

An AI planning system integrating Google Gemini CLI and Claude Code, providing functions such as automatic planning and execution, persistent context management, and MCP integration.

This project implements a personal context management server based on the MCP protocol, which is used to store and update users' personalized data, enabling AI assistants to maintain memory across sessions and provide personalized services.

The MCP Server is a lightweight system for managing contextual data of AI models to enhance the response capabilities of AI applications.

An MCP server for persisting development memory across projects, automatically capturing and organizing development context, code changes, and user interactions. Currently, it is hindered by file system access and JSON response handling issues.

An MCP server for expanding the context window/memory of AI agents, supporting storage, retrieval, and management of memories, including semantic search functionality.

The MCP plugin of ElizaOS implements the integration with the Model Context Protocol (MCP), enabling agents to connect to multiple MCP servers and obtain resource, prompt, and tool support.

The MCP Python SDK is a Python development toolkit that implements the Model Context Protocol (MCP), allowing developers to build standardized MCP servers and clients to provide context data, tools, and interaction templates for LLM applications.