semantic-code-mcp

# semantic-code-mcp MCP server that provides semantic code search for Claude Code. Instead of iterative grep/glob, it indexes your codebase with embeddings and returns ranked results by meaning. Supports **Python**, **Rust**, and **Markdown** — more languages planned. ## How It Works ``` Claude Code ──(MCP/STDIO)──▶ semantic-code-mcp server │ ┌───────────────┼───────────────┐ ▼ ▼ ▼ AST Chunker Embedder LanceDB (tree-sitter) (sentence-trans) (vectors) ``` 1. **Chunking** — tree-sitter parses source files into functions, classes, methods, structs, traits, markdown sections, etc. 2. **Embedding** — sentence-transformers encodes each chunk (all-MiniLM-L6-v2, 384d) 3. **Storage** — vectors stored in LanceDB (embedded, like SQLite) 4. **Search** — hybrid semantic + keyword search with recency boosting Indexing is incremental (mtime-based) and uses `git ls-files` for fast file discovery. The embedding model loads lazily on first query. ## Installation ### macOS / Windows PyPI ships CPU-only torch on these platforms, so no extra flags are needed (~1.7GB install). ```bash uvx semantic-code-mcp ``` **Claude Code integration:** ```bash claude mcp add --scope user semantic-code -- uvx semantic-code-mcp ``` ### Linux > [!IMPORTANT] > Without the `--index` flag, PyPI installs CUDA-bundled torch (~3.5GB). Unless you need GPU acceleration (you don't — embeddings run on CPU), use the command below to get the CPU-only build (~1.7GB). ```bash uvx --index pytorch-cpu=https://download.pytorch.org/whl/cpu semantic-code-mcp ``` **Claude Code integration:** ```bash claude mcp add --scope user semantic-code -- \ uvx --index pytorch-cpu=https://download.pytorch.org/whl/cpu semantic-code-mcp ``` <details> <summary>Claude Desktop / other MCP clients (JSON config)</summary> ```json { "mcpServers": { "semantic-code": { …