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.

Oracle Vector DB MCP Server for Google ADK 7 tools — connect in under 2 minutes

Name: Oracle Vector DB
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 7 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Oracle Vector DB as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="oracle_vector_db_agent",
    instruction=(
        "You help users interact with Oracle Vector DB "
        "using 7 available tools."
    ),
    tools=[mcp_tools],
)

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* 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.

Google ADK natively supports Oracle Vector DB as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 7 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

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 Google ADK 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 Google ADK via MCP

Follow these steps to integrate the Oracle Vector DB MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 7 tools from Oracle Vector DB via MCP

Why Use Google ADK with the Oracle Vector DB MCP Server

Google ADK provides unique advantages when paired with Oracle Vector DB through the Model Context Protocol.

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Oracle Vector DB

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine Oracle Vector DB tools with BigQuery, Vertex AI, and Cloud Functions

Oracle Vector DB + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Oracle Vector DB MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Oracle Vector DB and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Oracle Vector DB tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Oracle Vector DB regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Oracle Vector DB

Oracle Vector DB MCP Tools for Google ADK (7)

These 7 tools become available when you connect Oracle Vector DB to Google ADK 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 Google ADK

Ready-to-use prompts you can give your Google ADK 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 Google ADK

Common issues when connecting Oracle Vector DB to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Oracle Vector DB + Google ADK FAQ

Common questions about integrating Oracle Vector DB MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.