How to Use the Federal Register API MCP in LlamaIndex

Q: How does LlamaIndex store data retrieved from the Federal Register API?

Your agent uses the MCP tool searchfederaldocuments to pull document nodes. LlamaIndex then indexes these nodes into your chosen vector store for future semantic queries.

Q: Can I filter Federal Register API search results by agency in LlamaIndex?

Yes, you use listfederalagencies to get the list of active agencies, then configure your query engine to filter retrieved documents based on those agency names.

Q: How do I set up the Federal Register API MCP Server in LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient. You then convert the server tools using McpToolSpec and pass them directly to your agent.

Q: What tool gets the full text of a regulation?

Use getfederaldocumentdetails to retrieve the full metadata and HTML links. Your agent then reads the HTML content to index the complete text.

Index live US regulatory data directly into your LlamaIndex vector stores to ground your RAG applications in real policy.

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Connect Federal Register API MCP to LlamaIndex

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

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Index live regulatory searches into vector stores

This MCP Server lets your LlamaIndex agent run the `search_federal_documents` tool to gather the latest policy changes. Instead of just reading the text, your agent indexes the results directly into a vector database for semantic search. This setup prevents hallucinations by forcing your agent to ground its answers in actual, retrieved document text. You query your index later, and the system pulls from real, verified federal filings.

Build RAG pipelines around agency metadata

The `list_federal_agencies` tool extracts structured metadata about active government departments. Your LlamaIndex pipeline uses this MCP integration to build a searchable directory of regulatory bodies. You combine this directory with your existing document stores to filter search results by specific agencies. It ensures your queries only return policies from the relevant departments.

Fetch and index deep document details

Your agent uses the `get_federal_document_details` tool to retrieve full metadata and HTML links for target documents. It parses this unstructured HTML and stores it in your index. Before running large ingestion jobs, the agent calls `check_api_status` to confirm the federal endpoint is responsive. This prevents empty index writes and saves API rate limits.

Setup guide

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

Your agent uses the MCP tool `search_federal_documents` to pull document nodes. LlamaIndex then indexes these nodes into your chosen vector store for future semantic queries.

Yes, you use `list_federal_agencies` to get the list of active agencies, then configure your query engine to filter retrieved documents based on those agency names.

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient`. You then convert the server tools using `McpToolSpec` and pass them directly to your agent.

Use `get_federal_document_details` to retrieve the full metadata and HTML links. Your agent then reads the HTML content to index the complete text.

No, Vinkius executes the MCP Server in an isolated, ephemeral sandbox environment. Your queries, retrieved federal rules, and agency metadata remain strictly inside your local runtime.

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