Vector search indexing and querying workflows using MCP Vector Search, including setup, reindexing, auto-index strategies, and MCP integration.
Use mcp-vector-search to index codebases into ChromaDB and search via semantic embeddings. The recommended flow is setup (init + index + MCP integration), then search, and use index or auto-index to keep data fresh.
pip install mcp-vector-search
mcp-vector-search setup
mcp-vector-search search "authentication logic"
setup detects languages, initializes config, indexes the repo, and configures MCP integrations (Claude Code, Cursor, etc.).
mcp-vector-search index
mcp-vector-search index --force
mcp-vector-search index reindex --all --force
mcp-vector-search index reindex path/to/file.py
mcp-vector-search auto-index setup --method all
mcp-vector-search auto-index status
mcp-vector-search auto-index check --auto-reindex --max-files 10
mcp-vector-search auto-index teardown --method all
mcp-vector-search search "error handling patterns"
mcp-vector-search search "vector store initialization"
mcp-vector-search status
mcp-vector-search doctor
setup uses native claude mcp add when available, otherwise falls back to .mcp.json.
Typical .mcp.json entry:
{
"mcpServers": {
"mcp-vector-search": {
"type": "stdio",
"command": "uv",
"args": ["run", "mcp-vector-search", "mcp"],
"env": {
"MCP_ENABLE_FILE_WATCHING": "true"
}
}
}
}
uv for dev installs: uv sync --devsetup --force to rebuild config + index after tool upgradesMCP_ENABLE_FILE_WATCHING=truetoolchains/ai/protocols/mcpuniversal/main/mcp-builder