How to Use the GDACS MCP in Pydantic AI

Q: How do I integrate GDACS into my Pydantic AI workflow?

Install the MCP toolset and initialize the MCPToolset with your server URL. Pydantic AI then validates all incoming disaster data against your defined schemas at runtime.

Build type-safe disaster response agents with GDACS and Pydantic AI for reliable, validated hazard intelligence.

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Connect GDACS MCP to Pydantic AI

Create your Vinkius account to connect GDACS 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 GDACS with Pydantic AI

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Perplexity

Validated disaster data for Pydantic AI

Every response from `get_alert_detail` is parsed against your Pydantic models. If the API returns malformed data, your agent catches it immediately. This prevents silent errors in your alert pipeline. You ensure that only perfectly structured data reaches your notification systems.

Strict event tracking with Pydantic AI

Use `get_latest_droughts` or `get_latest_volcanoes` to pull current status reports. The agent validates every field, including alert levels and location coordinates. This gives you total confidence in the data. You don't have to write manual checks for null fields or unexpected types.

Type-safe impact assessment for complex agents

The `get_impacts` tool returns data that your agent casts into a rigid Pydantic structure. This allows for safe numerical analysis of economic and population impacts. It eliminates guessing games about data types. You write your logic knowing exactly what the disaster estimates look like.

Setup guide

Set up GDACS 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": {
        "gdacs-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about GDACS MCP in Pydantic AI

Install the MCP toolset and initialize the `MCPToolset` with your server URL. Pydantic AI then validates all incoming disaster data against your defined schemas at runtime.

The agent raises a validation error immediately. This stops the process before any bad data can corrupt your records or trigger a false alert.

Absolutely. The server is model-agnostic, meaning you can swap your underlying LLM without changing your disaster monitoring code.

The server supports SSE transports. This allows your agent to receive continuous updates as new disaster alerts appear in the feed.

The server operates in a V8 Isolate sandbox on our infrastructure. We ensure that your disaster query parameters and returned alert data are kept entirely private and ephemeral.

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