FEMA MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FEMA as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to FEMA. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in FEMA?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About FEMA MCP Server
Connect to the OpenFEMA public database through any AI agent and gain instant access to official data regarding disasters, emergency management, and federal assistance programs.
LlamaIndex agents combine FEMA tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Disaster Tracking — List official FEMA disaster declarations since 1953 and fetch detailed metadata for specific incidents natively
- Emergency Orchestration — List emergency management agencies by state to verify local coordination contacts flawlessly
- Assistance Analysis — Query housing assistance program data and individual assistance registrations to analyze recovery efforts natively
- Grant Inspection — Access hazard mitigation assistance projects and grant details to monitor community safety investments flawlessly
- Regional Insights — List official FEMA regions and web center locations to understand federal jurisdictional boundaries synchronously
- Public Data Access — Retrieve raw structured data from the official U.S. Federal Emergency Management Agency API without complex manual exports
The FEMA MCP Server exposes 11 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect FEMA to LlamaIndex via MCP
Follow these steps to integrate the FEMA MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 11 tools from FEMA
Why Use LlamaIndex with the FEMA MCP Server
LlamaIndex provides unique advantages when paired with FEMA through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine FEMA tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain FEMA tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query FEMA, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what FEMA tools were called, what data was returned, and how it influenced the final answer
FEMA + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the FEMA MCP Server delivers measurable value.
Hybrid search: combine FEMA real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query FEMA to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying FEMA for fresh data
Analytical workflows: chain FEMA queries with LlamaIndex's data connectors to build multi-source analytical reports
FEMA MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect FEMA to LlamaIndex via MCP:
get_disaster_applications
Get statistics on disaster assistance applications
get_emergency_agencies
List emergency management agencies by state
get_fema_regions
List official FEMA regions
get_fema_web_centers
List FEMA web center locations
get_hazard_mitigation_grants
Get data on hazard mitigation assistance projects
get_housing_assistance
Get data regarding FEMA housing assistance programs
get_individuals_program
Get data on individuals and households program registrations
get_me
Get current API status
get_public_assistance_applicants
List applicants for FEMA public assistance
get_registration_intake
Get data from individual and household program registrations
list_disaster_declarations
List recent official FEMA disaster declarations
Example Prompts for FEMA in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with FEMA immediately.
"List recent disaster declarations in Florida."
"Get emergency management agency info for California."
"Show hazard mitigation projects in Texas."
Troubleshooting FEMA MCP Server with LlamaIndex
Common issues when connecting FEMA to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFEMA + LlamaIndex FAQ
Common questions about integrating FEMA MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect FEMA with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect FEMA to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.