How to Use the FEMA MCP in LlamaIndex

Q: How does LlamaIndex index FEMA grant data?

LlamaIndex converts the JSON output from gethazardmitigationgrants into document nodes. These nodes are then vectorized and stored, allowing you to run semantic searches across project descriptions.

Q: Can I use this FEMA MCP Server to find local contacts in LlamaIndex?

Yes, you can query getemergencyagencies to pull state contacts. LlamaIndex indexes these agencies so your agent can retrieve them instantly during a simulated crisis.

Index FEMA disaster declarations and grant records into your LlamaIndex vector store using this MCP Server.

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Connect FEMA MCP to LlamaIndex

Create your Vinkius account to connect FEMA 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 FEMA with LlamaIndex

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Build a searchable archive of FEMA declarations

`list_disaster_declarations` feeds raw historical emergency data directly into your indexing pipeline. LlamaIndex parses these records into nodes, making them instantly searchable via semantic queries. This eliminates reliance on static PDFs during post-disaster audits. Your agent queries the vector store to find similar past disasters, using real data instead of making things up.

Ground housing assistance metrics in reality

`get_housing_assistance` retrieves the exact dollar amounts allocated for temporary shelter. LlamaIndex stores this structured data alongside `get_individuals_program` records to give your RAG pipeline absolute mathematical accuracy. Your agent won't hallucinate assistance numbers when writing briefs. It pulls the exact figures straight from your indexed data, keeping your reports completely factual.

Query emergency agency contacts via this MCP Server

`get_emergency_agencies` pulls state-by-state contact directories directly into your index. This MCP Server lets your LlamaIndex agent search for local coordinators based on geographical proximity. You can merge this with `get_fema_web_centers` to build a localized emergency response map. The index updates dynamically, keeping your contact lists accurate without manual database entries.

Setup guide

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

LlamaIndex converts the JSON output from `get_hazard_mitigation_grants` into document nodes. These nodes are then vectorized and stored, allowing you to run semantic searches across project descriptions.

Yes, you can query `get_emergency_agencies` to pull state contacts. LlamaIndex indexes these agencies so your agent can retrieve them instantly during a simulated crisis.

By forcing the LLM to read directly from the indexed outputs of `get_disaster_applications`. LlamaIndex uses strict context injection, meaning the agent only answers using the retrieved FEMA data.

No, you can use any vector database. The tool retrieves the data, and LlamaIndex handles the storage wherever you configure it.

We run this MCP Server in an ephemeral sandbox that holds zero state. Your queries to `get_public_assistance_applicants` pass directly through to OpenFEMA, and no personal applicant records are ever stored or cached.

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