Retrieve current status details for the Smart Coding MCP server, including version, workspace configuration, indexing progress, and cache information to monitor the semantic 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.