A database-backed Model Context Protocol (MCP) server for managing structured project context, designed to be used by AI assistants and developer tools within IDEs and other interfaces.
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.
- Serves as an ideal backend for Retrieval Augmented Generation (RAG), providing AI with precise, queryable project memory.
- Provides structured context that AI assistants can leverage for prompt caching with compatible LLM providers.
- Manages database schema evolution using Alembic migrations, ensuring seamless updates and data integrity.