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- Add mcp-server/tools/search_docs.py with real embedding query - Update mcp-server/server.py to use new search tool - Add tests/rag/test_retrieval_topk.py with top-k retrieval tests - Query → embed → cosine → top-k (k=3–5) functionality - Return passages + metadata (doc_id, chunk_id, score) - Fallback to mock results when RAG components unavailable Awaits CI for merge.
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What
RAG retrieval tool with real embedding-based document search for MCP server.
Scope
Files Added/Modified
mcp-server/tools/search_docs.py- Real embedding query functionalitymcp-server/server.py- Updated to use new search tooltests/rag/test_retrieval_topk.py- Top-k retrieval tests with mocksDependencies
Testing
Await CI: Merge after all gates pass.