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How to Use the USAspending (Federal Spending) MCP in Google ADK

Model complex federal spending patterns on Google Cloud with Google ADK.

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Connect USAspending (Federal Spending) MCP to Google ADK

Create your Vinkius account to connect USAspending (Federal Spending) 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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Analyze agency budget resources.

Need to know an agency's financial standing? Use `get_agency_budgetary_resources` to pull budgetary resources and obligations for any fiscal year. The data includes a breakdown of specific offices or sub-agencies using `get_agency_sub_agencies`. This lets your agent build complex models in BigQuery, correlating current funding levels with historical spending patterns across Google Cloud's infrastructure.

Map spending by location.

Visualizing federal spending geographically is easy. Fire up `search_spending_by_geography` to query amounts by state or county. You can also track overall trends using `search_spending_over_time`, perfect for longitudinal analysis. With Google ADK's long-context reasoning, your agent processes these geographic and temporal datasets, allowing you to find hidden correlations across massive data volumes.

Deep dive into specific awards.

The `get_award` tool provides the granular details on any single federal award. For a full picture, your agent can pull related transactions using `get_transactions`, or look at the parent funding structure via `get_award_funding`. This depth of data is ideal for enterprise use cases where you're validating compliance and tracing funds across multiple interconnected records.

Setup guide

Set up USAspending (Federal Spending) 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. 1

    Install Google ADK

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

  2. 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. 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. 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 USAspending (Federal Spending) 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="USAspending (Federal Spending)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to USAspending (Federal Spending) tools via MCP.",
    tools=mcp_tools,
)

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by USAspending. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about USAspending (Federal Spending) MCP in Google ADK

Your agent connects the MCP Server to your Google Cloud environment. When you ask it a question, Gemini models process the tool results and execute complex queries across BigQuery using native integrations.
Yes. Use `get_disaster_overview` to get a high-level look at emergency funding, and then drill down into specific amounts using `get_disaster_award_amount`. This is excellent for immediate impact assessment.
This server manages award types, agency overviews, recipient details, and various forms of spending amounts. It’s a rich dataset for modeling enterprise resource allocation.
The `get_agency_budgetary_resources` tool gives you the baseline data. Your agent can then cross-reference this against actual spending records retrieved via `get_transactions`, letting you model variance.
The server touches data regarding recipient names, UEI identifiers, and award details. The architecture is built to work within secure enterprise environments on Google Cloud.

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