greatscottymac/context-portal

##（这是记忆库！）

           

一个数据库支持的模型上下文协议（MCP）服务器，用于管理结构化项目上下文，设计用于 IDE 及其他接口中的 AI 助手和开发工具。

什么是上下文门户MCP服务器（ConPort）？

上下文门户（ConPort）是你项目的记忆库。它是一个工具，帮助AI助手更好地理解你的具体软件项目，通过结构化存储决策、任务和架构模式等重要信息。可以把它看作是构建一个项目专属知识库，AI可以轻松访问并利用它给你更准确、更有帮助的回答。

它的作用：

• 跟踪项目决策、进度和系统设计。
• 存储自定义项目数据（如术语表或规范）。
• 帮助AI快速找到相关项目信息（类似智能搜索）。
• 使人工智能能够利用项目上下文以获得更好的响应（RAG）。
• 相比简单的文本文件存储库，管理、搜索和更新上下文更高效。

What is Context Portal MCP server (ConPort)?

Context Portal (ConPort) is your project’s memory bank. It’s a tool that helps AI assistants understand your specific software project better by storing important information like decisions, tasks, and architectural patterns in a structured way. Think of it as building a project-specific knowledge base that the AI can easily access and use to give you more accurate and helpful responses.

What it does:

• Keeps track of project decisions, progress, and system designs.
• Stores custom project data (like glossaries or specs).
• Helps AI find relevant project information quickly (like a smart search).
• Enables AI to use project context for better responses (RAG).
• More efficient for managing, searching, and updating context compared to simple text file-based memory banks.

ConPort provides a robust and structured way for AI assistants to store, retrieve, and manage various types of project context. It effectively builds a project-specific knowledge graph, capturing entities like decisions, progress, and architecture, along with their relationships. This structured knowledge base, enhanced by vector embeddings for semantic search, then serves as a powerful backend for Retrieval Augmented Generation (RAG), enabling AI assistants to access precise, up-to-date information for more context-aware and accurate responses.

It replaces older file-based context management systems by offering a more reliable and queryable database backend (SQLite per workspace). ConPort is designed to be a generic context backend, compatible with various IDEs and client interfaces that support MCP.

Key features include:

• Structured context storage using SQLite (one DB per workspace, automatically created).
• MCP server (context_portal_mcp) built with Python/FastAPI.
• A comprehensive suite of defined MCP tools for interaction (see “Available ConPort Tools” below).
• Multi-workspace support via workspace_id.
• Primary deployment mode: STDIO for tight IDE integration.
• Enables building a dynamic project knowledge graph with explicit relationships between context items.
• Includes vector data storage and semantic search capabilities to power advanced RAG.