How to Use the Federal Register MCP in LlamaIndex

Q: Can I query the Federal Register with LlamaIndex for past rules?

You use the searchdocuments tool to locate older filings. Once retrieved, these documents are added to your index for long-term reference and semantic querying.

Q: How do I filter documents in LlamaIndex?

You use the agency slug with getagency to narrow down your focus. This ensures that only relevant regulatory content enters your vector store, keeping your index clean.

Index live regulatory data into your RAG pipeline using the Federal Register MCP Server and LlamaIndex.

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Connect Federal Register MCP to LlamaIndex

Create your Vinkius account to connect Federal Register 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 Federal Register with LlamaIndex

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Perplexity

Ground AI answers in live data

Turn the results from `search_public_inspection` into searchable knowledge base nodes. You stop relying on static training data and start querying the most recent government notices. Your agent builds a semantic index from these document snippets. This ensures that every response provided is grounded in the actual text retrieved from the federal repository.

Unified regulatory knowledge base

Combine API outputs with your private documents to create a single source of truth. You use the `get_document` tool to ingest specific rules into your vector store for instant retrieval. This approach lets you ask questions about how new rules affect your existing policies. Your index keeps everything linked, making complex compliance research a simple semantic search.

Automate document retrieval

Configure your agents to pull data automatically based on date filters. You use `get_public_inspection_by_date` to keep your local index updated with daily releases. This removes the need for manual updates. Your RAG system stays current with the latest agency filings without you needing to trigger a manual refresh every morning.

Setup guide

Set up Federal Register 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 Federal Register 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 Federal Register tools.",
)
response = await agent.run("List recent Federal Register 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 Federal Register. 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 Federal Register MCP in LlamaIndex

Yes, the MCP tools provide the raw text that LlamaIndex then processes into embeddings. You simply call the tools and pass the resulting content into your indexing pipeline.

You use the `search_documents` tool to locate older filings. Once retrieved, these documents are added to your index for long-term reference and semantic querying.

The data is as current as the API allows. You trigger the tools on demand or via a scheduled script to pull the latest public inspection notices into your local index.

You use the agency slug with `get_agency` to narrow down your focus. This ensures that only relevant regulatory content enters your vector store, keeping your index clean.

Your API keys and local indexed data never leave your control. The Federal Register server acts strictly as a data provider, and no sensitive information from your RAG index is transmitted to the government endpoint.

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