Oracle Vector DB MCP, Ready to Go
Use Claude or Cursor to query your data. The Oracle Vector DB MCP lets you run vector similarity searches and manage indexes via AI agents.
No credit card required. Experience the power of this integration risk-free.
Run vector similarity searches and manage Oracle 23ai indexes.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Oracle Vector DB MCP Server?
Average time for the server to become ready for requests over the last 12 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with Oracle Vector DB: 7 Tools for Vector Similarity Search
Query your Oracle 23ai vector store, inspect schemas, and manage indexes using natural language.
List tables
See all the tables available in your current Oracle schema. This helps you quickly identify where your data lives.
Describe table
Get the full schema for a table, including data types. You can use this to find specific VECTOR columns.
Execute sql query
Run custom SQL queries against your Oracle database via ORDS. Keep your row counts low to ensure the AI stays stable.
Vector search
Execute native VECTOR_DISTANCE queries to find similar neighbors. This works with both cosine and Euclidean metrics.
List vector indexes
See a list of all HNSW and IVF vector indexes. Use this to check your index status across different tables.
Table stats
Get row counts and optimizer statistics for your tables. It's great for planning your capacity and tuning performance.
Get database version
Check the exact Oracle DB runtime version banner. Use this to confirm your instance supports 23ai vector features.
One MCP enables access. Vinkius turns MCPs into production-ready infrastructure.
You're looking at one of 5,700+ managed MCPs. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
Oracle Vector DB for Enterprise RAG and Vector Similarity
The ML engineer who needs to test RAG pipelines on production data without leaving their IDE. The DBA who wants to check vector indexes using natural language. The enterprise data team tired of switching between SQL tools and AI chat windows.
ML Engineer
Testing embedding queries against real production data to refine RAG accuracy.
Data Engineer
Mapping out vector columns and verifying schema types for new embedding pipelines.
Database Administrator
Checking HNSW index status and optimizer stats without opening a dedicated SQL client.
Frequently Asked Questions
Does the Oracle Vector DB MCP work with my existing Oracle database? +
Yes, it works with Oracle Database 23ai. It connects to your existing setup via Oracle ORDS, allowing your AI agent to interact with your current tables and vector columns.
Can I use the Oracle Vector DB MCP for RAG applications? +
Absolutely. It's designed to help you build RAG pipelines by letting your agent perform native vector similarity searches and retrieve relevant context from your Oracle data.
Do I need to move my data to a new database to use this? +
No, you don't have to move anything. This MCP lets you use your existing Oracle 23ai instance as your vector store, keeping your data centralized and secure.
How does the Oracle Vector DB MCP handle similarity searches? +
It uses Oracle's native VECTOR_DISTANCE functions. You can ask your agent to find similar items using either cosine or Euclidean metrics directly on your vector columns.
Can I use this MCP to manage my database indexes? +
Yes, you can use it to list and inspect HNSW and IVF vector indexes. This helps you keep track of your AI search infrastructure without leaving your AI client.
Is it safe to run SQL queries through an AI agent? +
Yes, the MCP connects via Oracle ORDS. You can run ad-hoc queries for analysis, and the tool includes reminders to limit row counts to keep your AI agent's responses stable.
Does it work with Oracle Autonomous Database? +
Yes. Oracle Autonomous Database on OCI has ORDS enabled by default. Use the ORDS URL from your ADB instance (e.g., https://xxxxx.adb.us-ashburn-1.oraclecloudapps.com/ords), your schema name (typically ADMIN), and the admin credentials. The VECTOR type is available on all 23ai-compatible instances.
Can I run arbitrary SQL — not just vector searches? +
Yes. The execute_sql_query tool accepts any valid Oracle SQL statement and returns results through ORDS. Add FETCH FIRST N ROWS ONLY to keep payloads manageable. This makes the agent useful for relational queries too, not just vector operations.
Which distance metrics are available for vector search? +
Oracle 23ai supports COSINE and EUCLIDEAN (L2) distance metrics natively via VECTOR_DISTANCE. Specify the metric when running a search — cosine is recommended for most text embedding use cases, while L2 works better for image or audio embeddings.
Your AI, connected to everything.
No credit card required · Free tier available
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