pgvector (Vector Database) MCP Server for Google ADK 6 tools — connect in under 2 minutes
Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add pgvector (Vector Database) as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.
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Vinkius supports streamable HTTP and SSE.
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="pgvector_vector_database_agent",
instruction=(
"You help users interact with pgvector (Vector Database) "
"using 6 available tools."
),
tools=[mcp_tools],
)
* 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 pgvector (Vector Database) MCP Server
Connect your PostgreSQL + pgvector database to any AI agent and manage vector embeddings, similarity searches, and index optimizations through natural conversation.
Google ADK natively supports pgvector (Vector Database) as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 6 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 — Run nearest-neighbor queries using cosine, L2, or inner product distance metrics across millions of embeddings with a single prompt.
- Table Management — Discover which tables contain vector columns, create new embedding tables with custom dimensions, and inspect your schema.
- Embedding CRUD — Insert, update, and delete individual vector entries with metadata, keeping your knowledge base fresh and accurate.
- Index Optimization — Create HNSW or IVFFlat indexes on vector columns to accelerate approximate nearest-neighbor (ANN) queries by orders of magnitude.
The pgvector (Vector Database) MCP Server exposes 6 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 pgvector (Vector Database) to Google ADK via MCP
Follow these steps to integrate the pgvector (Vector Database) 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 6 tools from pgvector (Vector Database) via MCP
Why Use Google ADK with the pgvector (Vector Database) MCP Server
Google ADK provides unique advantages when paired with pgvector (Vector Database) 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 pgvector (Vector Database)
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 pgvector (Vector Database) tools with BigQuery, Vertex AI, and Cloud Functions
pgvector (Vector Database) + Google ADK Use Cases
Practical scenarios where Google ADK combined with the pgvector (Vector Database) MCP Server delivers measurable value.
Enterprise data agents: ADK agents query pgvector (Vector Database) and cross-reference results with internal databases for comprehensive analysis
Multi-modal workflows: combine pgvector (Vector Database) tool responses with Gemini's vision and language capabilities in a single agent
Automated compliance checks: schedule ADK agents to query pgvector (Vector Database) regularly and flag policy violations or configuration drift
Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including pgvector (Vector Database)
pgvector (Vector Database) MCP Tools for Google ADK (6)
These 6 tools become available when you connect pgvector (Vector Database) to Google ADK via MCP:
create_index
Create vector index
create_table
Create vector table
delete_vector
Delete a vector
insert_vector
Insert a vector
list_tables
List tables
search_vectors
Vector similarity search
Example Prompts for pgvector (Vector Database) in Google ADK
Ready-to-use prompts you can give your Google ADK agent to start working with pgvector (Vector Database) immediately.
"Show me all tables with vector columns in my database."
"Search for the 5 most similar documents to this query in the document_chunks table."
"Create a new table called 'support_tickets' with 1536-dimension vectors and an HNSW index."
Troubleshooting pgvector (Vector Database) MCP Server with Google ADK
Common issues when connecting pgvector (Vector Database) to Google ADK through the Vinkius, and how to resolve them.
McpToolset not found
pip install --upgrade google-adkpgvector (Vector Database) + Google ADK FAQ
Common questions about integrating pgvector (Vector Database) MCP Server with Google ADK.
How does Google ADK connect to MCP servers?
Can ADK agents use multiple MCP servers?
Which Gemini models work best with MCP tools?
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Connect pgvector (Vector Database) to Google ADK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.