Retrieve current server status including version, workspace path, model configuration, indexing progress, and cache details to monitor the semantic code search system's operational state.
Create a vector index for semantic code search by generating embeddings with tree-sitter and Jina AI, enabling efficient and accurate querying of source code files.
Identify similar code snippets within a project by analyzing syntax and structure. Specify a code snippet, language, and similarity threshold to retrieve matching code locations for efficient code comparison and review.
A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.
Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.