Oracle Vector DB MCP Server for Cursor 7 tools — connect in under 2 minutes
Cursor is an AI-first code editor built on VS Code that integrates LLM-powered coding assistance directly into the development workflow. Its Agent mode enables autonomous multi-step coding tasks, and MCP support lets agents access external data sources and APIs during code generation.
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{
"mcpServers": {
"oracle-vector-db": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
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About 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.
Cursor's Agent mode turns Oracle Vector DB into an in-editor superpower. Ask Cursor to generate code using live data from Oracle Vector DB and it fetches, processes, and writes — all in a single agentic loop. 7 tools appear alongside file editing and terminal access, creating a unified development environment grounded in real-time information.
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
The Oracle Vector DB MCP Server exposes 7 tools through the Vinkius. Connect it to Cursor in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Oracle Vector DB to Cursor via MCP
Follow these steps to integrate the Oracle Vector DB MCP Server with Cursor.
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
Save the file
Cursor will automatically detect the new MCP server
Start using Oracle Vector DB
Open Agent mode in chat and ask: "Using Oracle Vector DB, help me..." — 7 tools available
Why Use Cursor with the Oracle Vector DB MCP Server
Cursor AI Code Editor provides unique advantages when paired with Oracle Vector DB through the Model Context Protocol.
Agent mode turns Cursor into an autonomous coding assistant that can read files, run commands, and call MCP tools without switching context
Cursor's Composer feature can generate entire files using real-time data fetched through MCP — no copy-pasting from external dashboards
MCP tools appear alongside built-in tools like file reading and terminal access, creating a unified agentic environment
VS Code extension compatibility means your existing workflow, keybindings, and extensions all work alongside MCP tools
Oracle Vector DB + Cursor Use Cases
Practical scenarios where Cursor combined with the Oracle Vector DB MCP Server delivers measurable value.
Code generation with live data: ask Cursor to generate a security report module using live DNS and subdomain data fetched through MCP
Automated documentation: have Cursor query your API's tool schemas and generate TypeScript interfaces or OpenAPI specs automatically
Infrastructure-as-code: Cursor can fetch domain configurations and generate corresponding Terraform or CloudFormation templates
Test scaffolding: ask Cursor to pull real API responses via MCP and generate unit test fixtures from actual data
Oracle Vector DB MCP Tools for Cursor (7)
These 7 tools become available when you connect Oracle Vector DB to Cursor via MCP:
describe_table
Describe table columns and explicit data types including VECTORs
execute_sql_query
WARNING: Output payload size is inherently limited, restrict rows fetched (FETCH FIRST 100 ROWS ONLY) to ensure stability. Execute arbitrary SQL query against the Oracle runtime via ORDS
get_database_version
Get exact Oracle DB Runtime version banner
list_tables
List accessible tables in the current Oracle schema
list_vector_indexes
List specialized AI Vector search indexes (HNSW, IVF) instantiated
table_stats
Get table cardinality and optimizer statistics
vector_search
1, -0.4, 0.5]` against a strict `VECTOR` column natively inside Oracle DB, sorting and fetching the nearest neighbors. Execute Vector similarity search via Oracle 23ai native VECTOR_DISTANCE
Example Prompts for Oracle Vector DB in Cursor
Ready-to-use prompts you can give your Cursor agent to start working with Oracle Vector DB immediately.
"Show me all tables in my schema that have VECTOR columns."
"Find the 5 most similar documents to this embedding using cosine distance."
"What version of Oracle is running and does it support vectors?"
Troubleshooting Oracle Vector DB MCP Server with Cursor
Common issues when connecting Oracle Vector DB to Cursor through the Vinkius, and how to resolve them.
Tools not appearing in Cursor
Server shows as disconnected
Oracle Vector DB + Cursor FAQ
Common questions about integrating Oracle Vector DB MCP Server with Cursor.
What is Agent mode and why does it matter for MCP?
Where does Cursor store MCP configuration?
mcp.json file. You can configure servers at the project level (.cursor/mcp.json in your project root) or globally (~/.cursor/mcp.json). Project-level configs take precedence.Can Cursor use MCP tools in inline edits?
How do I verify MCP tools are loaded?
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TypeScript toolkit for building AI-powered web applications.
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Python framework for orchestrating collaborative AI agent crews.
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Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Oracle Vector DB to Cursor
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.
