How to Use the USAspending (Federal Spending) MCP in LlamaIndex

Q: How do I find historical spending data using USAspending (Federal Spending) with LlamaIndex?

You run searchnewawardsovertime to get time periods. By indexing the resulting award count and dates, your LlamaIndex client allows you to query past trends conversationally months later.

Q: What's the best way to compare agency funding levels using USAspending (Federal Spending) with LlamaIndex?

Run getagencybudgetaryresources for multiple agencies. Indexing these separate budgetary reports allows your agent to answer comparative questions, like 'Which of these three agencies had more resources in 2021?'

Q: Can LlamaIndex help analyze spending related to disaster funding using USAspending (Federal Spending)?

Yes. Querying getdisasteroverview and getdisasterawardamount provides structured data on emergency funds. Indexing this data means your AI client can answer complex, contextual questions about specific disaster spending.

Q: If I need to track a specific award's history using USAspending (Federal Spending), how does LlamaIndex help?

You first call getaward for the main details. Then, running searchsubawards links related agreements. Indexing these connections lets your agent build a complete, searchable audit trail of that specific award.

Turn USAspending (Federal Spending) data into a searchable knowledge graph with LlamaIndex.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect USAspending (Federal Spending) MCP to LlamaIndex

Create your Vinkius account to connect USAspending (Federal Spending) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup USAspending (Federal Spending) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Indexing spending trends across time using MCP Server.

Run `search_spending_over_time` to get aggregated transaction amounts over years. The unique power here is that this output becomes part of a searchable knowledge base within LlamaIndex. You can query historical patterns and ask follow-up questions grounded in the data. Don't just look at charts; index them. This lets your AI client recall specific spending levels from previous sessions or complex multi-year analyses.

Finding deep recipient information with LlamaIndex.

Start by using `autocomplete_recipient` to search for a name and UEI. Then, passing the resulting identifier to `get_recipient` pulls all associated details. The LlamaIndex client indexes this specific record data. Later, if you ask about that recipient again, your agent won't hallucinate; it retrieves the original facts from the indexed API result.

Analyzing agency spending structures with MCP Server.

To understand an agency's full scope, query `get_agency_awards` to get a summary of transactions and obligations. You can then use `get_sub_agencies` to list its smaller offices and the obligated amounts they hold. LlamaIndex combines these structured inputs into one cohesive knowledge entry that answers 'how much money does this entire office have?'

Setup guide

Set up USAspending (Federal Spending) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all USAspending (Federal Spending) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to USAspending (Federal Spending) tools.",
)
response = await agent.run("List recent USAspending (Federal Spending) data")

Get your connection token →

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect USAspending (Federal Spending) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about USAspending (Federal Spending) MCP in LlamaIndex

You run `search_new_awards_over_time` to get time periods. By indexing the resulting award count and dates, your LlamaIndex client allows you to query past trends conversationally months later.

Run `get_agency_budgetary_resources` for multiple agencies. Indexing these separate budgetary reports allows your agent to answer comparative questions, like 'Which of these three agencies had more resources in 2021?'

Yes. Querying `get_disaster_overview` and `get_disaster_award_amount` provides structured data on emergency funds. Indexing this data means your AI client can answer complex, contextual questions about specific disaster spending.

You first call `get_award` for the main details. Then, running `search_subawards` links related agreements. Indexing these connections lets your agent build a complete, searchable audit trail of that specific award.

The server handles structured financial records: transaction details, recipient identifiers, agency budgets, subaward agreements, and detailed spending summaries. This depth of record allows for highly accurate knowledge indexing.

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