mcp-chat is an open-source and general-purpose MCP client tool for testing and evaluating MCP servers and proxies. It supports command-line interaction and web mode, can connect to various MCP servers (JS/Python/Docker), and provides functions such as chat history recording, model selection, and system prompt customization to help developers debug MCP services.

A Perl-based MCP server implementation that interacts with the Cursor IDE through standard input and output, providing direct access to its internal state (including chat and writing history).

Cursor chat history analysis tool that allows AI assistants to read local chat data through the MCP protocol, providing personalized programming assistance and pattern analysis

The SlimContext MCP server is an AI chat history compression tool based on the SlimContext library, providing two compression strategies for clients through the Model Context Protocol: token - based trimming compression and AI - driven intelligent summary compression.

Perplexity MCP Server is a Python-based interface service that provides tools for interacting with the Perplexity API, supporting query responses, maintaining chat history, and managing conversations. It configures models through environment variables and stores chat data locally, making it suitable for integration in development environments.

A TypeScript-based MCP server that provides a memory system for LLMs, supporting multi-platform chat and history record management.

An MCP server based on the Model Context Protocol for browsing, searching, exporting, and backing up Cursor AI chat history, supporting offline fast retrieval and cross-workspace migration.

The Perplexity MCP Server is a Python-based interface tool that provides functions to interact with the Perplexity API, supporting query responses, maintaining chat history, and managing conversations. You can configure the model through environment variables, and it has features such as local storage of chat data, streaming responses, and a Web UI.

An MCP server based on TypeScript that implements a memory system for LLMs, supports multi - provider chatting, and maintains conversation history.

A full-stack web application based on FastAPI that provides a dual AI assistant experience (chatbot and tool-based agent), supports user authentication, real-time chat, history records, and file analysis, and can be deployed on Heroku.