Oracle Vector DB MCP. Run sophisticated vector searches right from your agent.
Oracle Vector DB MCP connects your AI agent directly to Oracle Database 23ai's native vector capabilities. You can execute complex VECTOR_DISTANCE similarity searches, inspect schema details, and manage indexes—all from natural conversation. It lets ML engineers test RAG pipelines and data teams run advanced analytics without context switching.
Give Claude and any AI agent real-world access
Execute native Oracle 23ai VECTOR_DISTANCE queries using cosine or Euclidean metrics against specified vector columns.
List accessible tables and describe column types, identifying which ones store vector embeddings.
Run custom SQL queries against the Oracle runtime environment for general data retrieval and analysis.
List all instantiated AI vector search indexes, such as HNSW or IVF, across your database tables.
Get table cardinality and optimizer statistics to plan for capacity and tune query performance.
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What AI agents can do with Oracle Vector DB: 7 Tools Available
These tools allow you to query the database schema, run complex SQL queries, and execute advanced vector distance calculations natively within your AI client.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Oracle Vector DB MCPList Tables
Lists every table available in the current Oracle schema for immediate reference.
Describe Table
Provides a detailed breakdown of a specific table's columns and their explicit data...
Execute Sql Query
Allows you to run any arbitrary SQL query against the Oracle runtime environment via...
Vector Search
Performs a vector similarity search using native Oracle 23ai VECTOR_DISTANCE on...
List Vector Indexes
Retrieves a list of specialized AI vector indexes (HNSW, IVF) that have been created...
Table Stats
Gathers crucial data on table cardinality and optimizer statistics for performance planning.
Get Database Version
Returns the exact Oracle DB Runtime version banner, verifying compatibility with 23ai features.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Oracle Vector DB, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
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Database access used to mean leaving your AI workspace
Today, if you want your agent to query a production database, you're out of luck. You have to copy the data into memory or use complex connectors that force context switching, breaking your flow between analysis and writing.
Now, this MCP keeps the connection live. Your AI client talks directly to Oracle via these tools. It lets you run advanced operations like `vector_search` without ever leaving your agent's chat window.
Accessing Data Structures with Oracle Vector DB MCP
Manual data exploration requires logging into a separate tool, running `list_tables`, then opening another tab to run schema details via `describe_table`, and finally writing the SQL query manually.
With this MCP, you simply ask your agent. It handles the full sequence—listing, describing, executing, or searching—and returns the final answer in plain text.
What Oracle Vector DB MCP does for your AI
This MCP brings deep database functionality into your AI agent workflow. Your agent can run native Oracle 23ai VECTOR_DISTANCE queries using cosine or Euclidean metrics against any table containing vector columns. Need to know what's in the schema? You can list all tables and describe column types, making sure to spot those key VECTOR-enabled columns for embedding storage.
If you need raw data, execute arbitrary SQL queries through ORDS for ad-hoc analysis. Beyond querying, your agent handles index management, allowing it to list specialized AI vector indexes (HNSW or IVF) and check overall table statistics. It's all integrated via Vinkius, giving you full control over complex database operations without writing a single line of boilerplate code.
019d75eb-9634-7229-8a1b-e0cd1fe251a5 How to set up Oracle Vector DB MCP
The bottom line is that you speak naturally to your AI client, and it translates those requests into complex, secure Oracle database actions.
Subscribe the MCP using your Oracle ORDS URL, Schema, Username, and Password credentials.
Connect your preferred AI client (like Cursor or Claude) to this MCP via Vinkius.
Instruct your agent to perform a task, such as 'Find documents similar to X' or 'List all tables with vector columns.' The tool executes the necessary database call.
Who uses Oracle Vector DB MCP
ML engineers who need to test RAG pipelines on production data; DBAs who want conversational ways to inspect vector indexes; or data scientists running ad-hoc analyses against structured records.
Testing embedding queries and similarity searches using the vector_search tool during RAG pipeline development.
Using tools like list_tables and table_stats to inspect vector indexes, schema configurations, and overall table health through conversation.
Running complex ad-hoc reports using execute_sql_query, or finding related knowledge by running a similarity search.
Benefits of connecting Oracle Vector DB MCP
You get to run advanced, native VECTOR_DISTANCE queries directly through your AI client. This means you can test complex retrieval-augmented generation (RAG) patterns without writing code or switching tools.
Instead of manually checking SQL Developer, you simply ask the MCP to list tables and describe columns. It's instant visibility into which data sets are ready for embedding storage.
Complex database operations—like running arbitrary queries using execute_sql_query—are handled conversationally. You just tell your agent what data you need, and it gets it.
The MCP manages index overhead by letting you list specialized AI vector indexes (list_vector_indexes). This gives ML engineers clear visibility into the performance infrastructure.
You can plan for growth using table_stats. Instead of guessing if a table is large enough, your agent checks its actual cardinality and optimizer statistics.
Oracle Vector DB MCP use cases
Finding related documents in an enterprise knowledge base
A data scientist needs to find documentation related to 'API Rate Limits' from a massive set of records. They ask their agent, which uses vector_search with cosine distance, and immediately gets the top 5 most relevant document IDs and snippets.
Auditing vector index setup on a new database
A DBA wants to verify if all necessary indexes are in place for a project. They instruct their agent to run list_vector_indexes and check the schema using describe_table, confirming both the structure and the indexing status.
Running ad-hoc financial reports
A business analyst needs to quickly count records for a specific quarter that aren't covered by standard dashboards. They use the agent to run an execute_sql_query on the raw transaction table, getting immediate counts and aggregates.
Verifying database feature compatibility
An ML engineer is setting up a new RAG pipeline and needs confidence that the target Oracle instance supports the latest features. They ask the agent to run get_database_version and check for vector support.
Oracle Vector DB MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Trying to write complex SQL manually
Opening a separate terminal or IDE, writing out the full query syntax, handling quoting issues, and then running it piece by piece.
Just tell your agent what you need. Use execute_sql_query in natural language; the MCP handles translating that into precise, runnable SQL.
Ignoring index status
Assuming a table has proper vector indexing simply because it contains VECTOR columns, leading to slow or failed searches.
Always run list_vector_indexes first. This confirms if the necessary HNSW or IVF indexes are actually instantiated on your tables.
Forgetting context details
Asking the agent to search a table without knowing its structure, resulting in an error because it doesn't know which columns exist.
Start by running describe_table on that dataset. You can then use this schema information when constructing your vector_search query.
When to use Oracle Vector DB MCP
Use this MCP if you need to treat your Oracle Database 23ai as a live, actionable data source for your AI agent. This is ideal for ML engineers building RAG systems or DBAs needing conversational oversight of vector assets. Don't use it if all you need is simple key-value lookups that don't involve complex structured data analysis or similarity search; then perhaps an external document storage tool suffices. You must use this MCP when your process requires running VECTOR_DISTANCE queries, managing HNSW indexes, or executing general SQL against the source of truth.
Frequently asked questions about Oracle Vector DB MCP
Does Oracle Vector DB MCP support all kinds of vector searches? +
Yes, it supports native Oracle 23ai VECTOR_DISTANCE queries using either cosine or Euclidean metrics for similarity searching. The vector_search tool handles this.
What if I only need to see the column names in my database? +
You can use list_tables to get a list of all accessible tables, or run describe_table on a specific table name to get full details.
How do I check if my Oracle instance is ready for vector data? +
You should first use the get_database_version tool. This verifies your runtime version and confirms compatibility with 23ai features like VECTOR_DISTANCE.
Can I run queries that aren't related to vectors? +
Absolutely. The execute_sql_query tool lets you run any standard SQL query against the Oracle runtime, regardless of whether it involves vector data.
Does listing indexes cost money or resources? +
The MCP only reads metadata when you call list_vector_indexes. It reports on existing HNSW and IVF indices without performing any write operations.