How to Use the MTA MCP in Google ADK

Q: Can Google ADK agents filter specific MTA tools?

You can use a toolnames filter in your toolset configuration. This limits the agent to only the specific MTA transit functions you choose.

Bring MTA data into your Google ADK agent. Connect transit feeds to your enterprise infrastructure on Google Cloud.

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Connect MTA MCP to Google ADK

Create your Vinkius account to connect MTA to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup MTA with Google ADK

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Google ADK transit data integration

Query the `get_stations` tool to map the subway network directly into your agent's context. This maps station names to their exact coordinates for your analysis. Your agent can use this data to perform spatial lookups. It finds the nearest transit hub for a given address in your existing BigQuery datasets.

Live bus fleet monitoring

Expose `get_bus_vehicles` to your agent for real-time visibility into the city's bus fleet. You get position and heading data for every active unit. This is useful for fleet management tasks. Your agent tracks movement patterns and logs vehicle availability across different boroughs.

Service alert automation

Automate your incident response by calling `get_service_alerts`. Your agent filters these alerts to find disruptions that impact your specific transit routes. It triggers downstream workflows based on the severity level. You keep your users informed about construction or weather events as they happen.

Setup guide

Set up MTA 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 MTA 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="MTA_agent",
    model="gemini-2.0-flash",
    instruction="You have access to MTA tools via MCP.",
    tools=mcp_tools,
)

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Common questions about MTA MCP in Google ADK

Use the McpToolset class with your server URL. It bridges the MCP connection to your Gemini-powered agent seamlessly.

You can use a tool_names filter in your toolset configuration. This limits the agent to only the specific MTA transit functions you choose.

Yes. You can pull large sets of station data into the context. This allows for complex planning across the entire system.

The connection is managed via your endpoint token. Your agent interacts with the transit data in a sandboxed environment.

It handles public transit information like stop IDs, train positions, and service alerts. No personal user data is required to run these queries.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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