How to Use the Fiscal Data (U.S. Treasury) MCP in LlamaIndex

Index live Fiscal Data (U.S. Treasury) into LlamaIndex to ground your RAG applications in official government numbers.

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Connect Fiscal Data (U.S. Treasury) MCP to LlamaIndex

Create your Vinkius account to connect Fiscal Data (U.S. Treasury) 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.

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Setup Fiscal Data (U.S. Treasury) with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Semantic search for fiscal records

Use `get_mts_table_9` to pull monthly statement data and index it immediately. LlamaIndex turns these raw reports into searchable knowledge nodes for your application. This ensures your agent answers questions with verified government data. You avoid hallucinations by grounding every response in the specific rows returned by the Treasury API.

Unified knowledge base integration

Combine `query_dataset` results with your existing document corpus. Your agent queries both the official fiscal data and your internal files in a single pass. This makes your RAG pipeline smarter. The agent references real exchange rates alongside your internal strategy documents.

Live API data as context

Fetch real-time `get_rates_of_exchange` updates as part of your retrieval process. LlamaIndex caches these values in your vector store for instant access during inference. This keeps your application current. You no longer rely on stale, static files when the official Treasury rates are just one tool call away.

Setup guide

Set up Fiscal Data (U.S. Treasury) 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 Fiscal Data (U.S. Treasury) 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 Fiscal Data (U.S. Treasury) tools.",
)
response = await agent.run("List recent Fiscal Data (U.S. Treasury) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fiscal Data (U.S. Treasury). 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 Fiscal Data (U.S. Treasury) MCP in LlamaIndex

Yes. Use the MCP tool spec to fetch data and pass the output into your indexing pipeline. It transforms raw API responses into clean, queryable vector data.

It provides a direct link to the source of truth. By grounding your context in official Treasury data, you reduce the risk of the model guessing on financial figures.

Absolutely. The tools are compatible with all standard agent types, allowing the model to decide when it needs to fetch fresh data versus when it can rely on the index.

The data returns as JSON, which is perfect for parsing. You can flatten these objects before they hit your vector store to make them easier to search.

The server treats your requests as ephemeral. It does not log your internal data, as it only processes public government records through a secure, isolated tunnel.

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