How to Use the Fire Safety Calculator MCP in Pydantic AI

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Connect Fire Safety Calculator MCP to Pydantic AI

Create your Vinkius account to connect Fire Safety Calculator 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 Fire Safety Calculator with Pydantic AI

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Validate Code Compliance with Pydantic AI

You can run `calculate_max_walking_distance` and be certain of the output. Every response is validated against a strict schema, so you'll never get corrupted compliance data. It also helps determine if your structure meets minimum stair width requirements using `determine_min_stair_width`. The Pydantic validation means you trust the numbers.

Model-Safe Safety Calculations

Use this MCP to calculate everything from fire extinguisher quantity via `specify_extinguisher_requirement` to overall structural integrity. This toolset is model-agnostic, meaning its output format won't break your agent, regardless of which LLM you use. It also provides max walking distance compliance checks.

Build Reliable Multi-Step Agents

Because the MCP response is strictly typed, chaining multiple tools—like running `determine_min_stair_width` and then feeding its output into a full safety audit—works flawlessly. No silent failures, just hard validation errors if something goes wrong. This makes building complex compliance pipelines really reliable.

Setup guide

Set up Fire Safety Calculator 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": {
        "fire-safety-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Fire Safety Calculator transactions")
print(result.output)

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Common questions about Fire Safety Calculator MCP in Pydantic AI

The MCP output is validated against Pydantic models at runtime. This means if the server sends back unexpected data, your agent fails loudly with a validation error, not silently corrupted results.

The toolset requires occupancy load counts and structural measurements like area definitions, path lengths, and population density profiles to run calculations.

It calculates mandatory requirements for minimum stair width and maximum walking distance. The type-safe nature of the data ensures your agent processes these numbers correctly every time.

The server processes structural dimensions and occupancy counts. Data passes through Vinkius' zero-trust proxy, ensuring your keys are used only in transit and never stored locally.

Yes. The core feature is that if the API returns data that doesn't match the expected schema, the agent throws a clear validation error. No more hallucinated fields or silent failures.

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