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Google ADK Integration

Google Agent Development Kit (ADK) agent that connects to the OrionBelt Analytics MCP server. All 20+ tools are auto-discovered via ADK's native MCP support (MCPToolset).

Files

File Purpose
agent_example.py Interactive agent using Agent + Runner with MCP session management

Prerequisites

pip install google-adk

Setup

Start OrionBelt Analytics MCP server:

uv run server.py

Set your API key:

export GOOGLE_API_KEY=...

Quick Start

import asyncio
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.adk.tools.mcp_tool import MCPToolset
from google.genai import types

async def main():
    tools, cleanup = await MCPToolset.from_server(
        connection_params={"url": "http://localhost:9000/mcp"},
    )
    agent = Agent(
        name="orionbelt_analyst",
        model="gemini-2.0-flash",
        instruction="You are a database analyst. Use the tools to analyze schemas and generate SQL.",
        tools=tools,
    )

    session_service = InMemorySessionService()
    session = await session_service.create_session(
        app_name="orionbelt_analyst", user_id="user",
    )
    runner = Runner(
        agent=agent,
        app_name="orionbelt_analyst",
        session_service=session_service,
    )

    content = types.Content(
        role="user",
        parts=[types.Part(text="Connect to PostgreSQL and analyze the public schema")],
    )
    async for event in runner.run_async(
        user_id="user", session_id=session.id, new_message=content,
    ):
        if event.is_final_response() and event.content and event.content.parts:
            print(event.content.parts[0].text)

    await cleanup()

asyncio.run(main())

Auto-Discovered Tools

All MCP tools are automatically available. Key tools include:

Tool Description
connect_database Connect to PostgreSQL, Snowflake, Dremio, ClickHouse, or MySQL
list_schemas Discover available database schemas
analyze_schema Analyze schema structure with relationships
get_table_details Deep-dive into a specific table
generate_ontology Generate RDF/OWL ontology with SQL mappings
validate_sql_syntax Validate SQL syntax, security, and fan-trap risks
execute_sql_query Execute validated SQL with fan-trap protection
sample_table_data Sample rows from a table
generate_chart Create Plotly charts from query results
graphrag_query_context Natural language schema discovery via GraphRAG
graphrag_search Vector search across schema elements
graphrag_find_join_path Find join paths between tables
query_sparql Run SPARQL queries on the RDF store
get_server_info Server version and capabilities

Stdio Transport

For stdio transport:

tools, cleanup = await MCPToolset.from_server(
    command="uv",
    args=["run", "--directory", "/path/to/orionbelt-analytics", "server.py"],
)

Multi-Agent Example

Use ADK's sub-agent pattern:

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import MCPToolset

tools, cleanup = await MCPToolset.from_server(
    connection_params={"url": "http://localhost:9000/mcp"},
)

schema_agent = Agent(
    name="schema_analyst",
    model="gemini-2.0-flash",
    instruction="Analyze database schemas and generate ontologies.",
    tools=tools,
)

report_agent = Agent(
    name="report_writer",
    model="gemini-2.0-flash",
    instruction="Write data analysis reports. Delegate database queries to schema_analyst.",
    sub_agents=[schema_agent],
)

Deploy to Vertex AI Agent Engine

ADK agents can be deployed to Google Cloud:

from google.adk.agents import Agent
from google.adk.deploy import VertexAIAgentEngine

agent = Agent(name="orionbelt_analyst", model="gemini-2.0-flash", tools=tools)
engine = VertexAIAgentEngine(project="your-project", location="us-central1")
engine.deploy(agent)