How to Use the pgvector (Vector Database) MCP in Google ADK

Q: How do I perform a search in Google ADK using pgvector (Vector Database)?

Pass the search criteria to the searchvectors tool via your agent's toolset. It executes the query against your database and returns the results as structured data.

Connect your Google ADK agents to PostgreSQL vector storage for efficient retrieval and long-context reasoning.

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Connect pgvector (Vector Database) MCP to Google ADK

Create your Vinkius account to connect pgvector (Vector Database) to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup pgvector (Vector Database) with Google ADK

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Direct vector storage for Google ADK

Set up your database schema with `create_table` to store high-dimensional embeddings. It is the fastest way to bridge your relational data with AI-driven search. Call `create_index` once your data is populated to optimize lookup speeds. Your Google ADK agent triggers these commands as part of its automated maintenance tasks.

Perform fast lookups using MCP Server

Invoke `search_vectors` to find similar records based on user input. The server returns the top results directly to your agent's context window. You can chain this with other Google Cloud tools to analyze retrieved data. It ensures your agent has the right context before it starts reasoning.

Manage your database collections

Use `list_tables` to see which vector sets are currently available. This helps your agent decide which collection to query for a specific task. If you need to clean up, `delete_vector` removes individual items from your index. It gives you precise control over your database state.

Setup guide

Set up pgvector (Vector Database) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with pgvector (Vector Database) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="pgvector (Vector Database)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to pgvector (Vector Database) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about pgvector (Vector Database) MCP in Google ADK

It is. You define the server parameters in your McpToolset and the agent gains access to all six database tools.

Pass the search criteria to the `search_vectors` tool via your agent's toolset. It executes the query against your database and returns the results as structured data.

Yes, as long as your agent has network access to the PostgreSQL instance. The server acts as a standard MCP gateway for your database.

Access is restricted to users with valid PostgreSQL credentials. The server acts as a proxy, enforcing the permissions you set on your database tables.

It stores float-based embedding arrays. These are standard vector formats compatible with most common ML models.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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