How to Use the FEMA MCP in AutoGen

Q: How do AutoGen agents share FEMA data?

Agents pass the JSON payloads from tools like getdisasterapplications through their conversational history. This ensures all agents in the group chat are looking at the same application metrics.

Q: How do I register this FEMA MCP Server with AutoGen?

You use the mcpservertools adapter to load the FEMA tools. This automatically converts the schemas so your AutoGen AssistantAgent can invoke them during conversations.

Q: Is my FEMA registration intake data safe?

Yes, completely. This MCP Server processes all getregistrationintake queries inside a secure, zero-trust sandbox. No registration data is ever written to disk or logged outside your local AutoGen session.

Let AutoGen agents debate resource allocation using real-time FEMA data pulled via this MCP Server.

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Setup FEMA with AutoGen

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Run agent debates on FEMA disaster declarations

`list_disaster_declarations` provides the raw ground truth that your AutoGen agents use to trigger emergency protocols. One agent can analyze the declaration type while another validates budget constraints. This multi-agent setup prevents hasty decisions during high-stress scenarios. By forcing your agents to cross-reference declarations, you ensure that no response plan is drafted without verified data.

Audit grant distributions with multi-agent consensus

`get_hazard_mitigation_grants` pulls the funding history for local mitigation projects. An auditor agent checks these numbers against `get_public_assistance_applicants` to flag potential municipal double-dipping. You get a self-correcting analysis pipeline that doesn't need constant human oversight. The agents debate the discrepancies in your terminal, outputting a clean, verified list of anomalies.

Coordinate regional logistics using this MCP Server

`get_fema_regions` maps out the official boundaries for federal response efforts. This MCP Server allows your AutoGen coordination agents to assign tasks to the correct regional offices automatically. They can query `get_fema_web_centers` to locate active physical sites. The agents coordinate with each other to allocate simulated resources, ensuring realistic operational constraints.

Setup guide

Set up FEMA MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes FEMA tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="FEMA_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent FEMA data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="FEMA_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent FEMA data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Agents pass the JSON payloads from tools like `get_disaster_applications` through their conversational history. This ensures all agents in the group chat are looking at the same application metrics.

Yes, that is the standard AutoGen pattern. Your retrieval agent fetches the housing data, and a separate analyst agent reviews the numbers to draft a report.

You use the `mcp_server_tools` adapter to load the FEMA tools. This automatically converts the schemas so your AutoGen AssistantAgent can invoke them during conversations.

Yes, if your AutoGen agents decide to check multiple regions at once, they can trigger parallel calls. This speeds up data collection when querying large datasets like disaster applications.

Yes, completely. This MCP Server processes all `get_registration_intake` queries inside a secure, zero-trust sandbox. No registration data is ever written to disk or logged outside your local AutoGen session.

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