Clancey MCP: Long term memory for Claude Code (github.com)

The introduction of Clancey MCP marks a significant advancement for AI/ML practitioners using Claude Code by providing a powerful long-term memory tool that indexes conversations for semantic search. This server allows users to effortlessly retrieve information from past discussions, enabling quick solutions and context retrieval that enhance productivity. The auto-indexing feature ensures that new conversations are automatically cataloged in real-time, fostering a more efficient workflow for developers and data scientists. From a technical standpoint, Clancey MCP operates locally, utilizing LanceDB as its vector database and an all-MiniLM-L6-v2 model from Huggingface for fast, lightweight embeddings. This setup not only maintains user privacy but also ensures rapid search capabilities through parsed JSONL conversation histories, which are chunked into easily searchable segments. The implementation is straightforward, requiring just a few simple configuration steps, making it accessible for users to harness the benefits of semantic search, such as querying specific issues like bug fixes effectively. Overall, this development represents a meaningful leap forward in achieving more intelligent and context-aware AI coding environments.