Oracle Vector DB MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Oracle Vector DB as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Oracle Vector DB. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Oracle Vector DB?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
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.
LlamaIndex agents combine Oracle Vector DB tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Oracle Vector DB MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Oracle Vector DB
Why Use LlamaIndex with the Oracle Vector DB MCP Server
LlamaIndex provides unique advantages when paired with Oracle Vector DB through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Oracle Vector DB tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Oracle Vector DB tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Oracle Vector DB, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Oracle Vector DB tools were called, what data was returned, and how it influenced the final answer
Oracle Vector DB + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Oracle Vector DB MCP Server delivers measurable value.
Hybrid search: combine Oracle Vector DB real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Oracle Vector DB to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Oracle Vector DB for fresh data
Analytical workflows: chain Oracle Vector DB queries with LlamaIndex's data connectors to build multi-source analytical reports
Oracle Vector DB MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Oracle Vector DB to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Oracle Vector DB to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOracle Vector DB + LlamaIndex FAQ
Common questions about integrating Oracle Vector DB MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Oracle Vector DB with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Oracle Vector DB to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.