Oracle Vector DB MCP Server

Bring your Oracle Database 23ai vector capabilities directly into your AI agent workflow. Run VECTOR_DISTANCE similarity searches, inspect table schemas, execute SQL queries, and manage vector indexes — all through natural conversation.

What you can do

• Vector Similarity Search — Execute native Oracle 23ai VECTOR_DISTANCE queries with cosine or Euclidean metrics against any table with VECTOR columns
• Schema Inspection — List all tables in your schema and describe column types, spotting VECTOR-enabled columns for embedding storage
• SQL Execution — Run arbitrary SQL queries against Oracle via ORDS for ad-hoc analysis and data retrieval
• Vector Index Management — List all HNSW and IVF vector indexes instantiated across your tables
• Table Statistics — Get row counts and optimizer stats for capacity planning and query performance tuning
• Version Check — Verify your Oracle runtime version to confirm 23ai vector feature compatibility

How it works

1. Subscribe to this server
2. Enter your Oracle ORDS URL, Schema, Username, and Password
3. Start querying your vector store from Claude, Cursor, or any MCP-compatible client

Who is this for?

• Enterprise data teams — run vector searches against production Oracle databases without context-switching to SQL Developer
• ML engineers — test embedding queries on Oracle 23ai's native vector engine during RAG pipeline development
• DBAs — inspect vector indexes, table stats, and schema configurations through conversation