How to Use the FEMA MCP in Pydantic AI

Validate federal disaster records at runtime with type-safe Pydantic AI agent pipelines.

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Connect FEMA MCP to Pydantic AI

Create your Vinkius account to connect FEMA to Pydantic AI 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 Pydantic AI

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Enforce strict schemas on FEMA housing assistance data

The `get_housing_assistance` tool delivers structured housing program metrics directly into your Pydantic AI agent from our MCP Server. Because this framework validates every incoming payload against strict Python types, any unexpected API changes trigger immediate, loud validation errors. This strictness prevents your models from acting on corrupted or hallucinated housing metrics. Your agent can confidently process financial assistance stats knowing the schema is locked down.

Verify disaster applications with type-safe tools

The `get_disaster_applications` tool queries real-time registration stats that your Pydantic AI agent parses into clean Python models. If the federal database returns a malformed field, your pipeline catches it before it reaches your downstream logic. This model-agnostic approach works whether you run local models or commercial APIs. It ensures your emergency reporting pipelines remain completely reliable under heavy stress.

Connect your agent to an MCP Server with zero drift

The `get_fema_web_centers` tool exposes geographical coordination points to your codebase using our managed MCP Server. Pydantic AI integrates this toolset natively, mapping the server's JSON schemas directly to runtime Python definitions. This setup eliminates schema drift between the federal API and your application code. You get a reliable, type-safe interface for mapping physical help centers during active crises.

Setup guide

Set up FEMA MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "fema-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to FEMA tools.",
)

result = await agent.run("List recent FEMA transactions")
print(result.output)

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Common questions about FEMA MCP in Pydantic AI

The framework intercepts JSON responses from endpoints like `get_individuals_program` and parses them against Pydantic models. If the fields don't match your defined types, it raises a validation error immediately.

Import the MCPToolset class and pass our streamable HTTP endpoint to the constructor. Then, register this toolset in your agent's configuration to expose all eleven emergency tools.

Yes, because the framework is model-agnostic, you can use local models to process data from `list_disaster_declarations`. The type-safety layer works identically regardless of your chosen model.

The tool call will return a standard HTTP error which your agent can catch and handle. You can write fallback logic to query `get_me` to check if the connection is active.

We handle all API transactions through secure, single-use V8 isolates that dissolve immediately after execution. Your housing assistance records are never cached, logged, or exposed to third parties, keeping your compliance audits clean.

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