How to Use the FIRE Calculator MCP in Pydantic AI

Build strictly typed retirement planners using Pydantic AI and the FIRE Calculator MCP.

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Connect FIRE Calculator MCP to Pydantic AI

Create your Vinkius account to connect FIRE Calculator to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup FIRE Calculator with Pydantic AI

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Enforce Strict Financial Math

This MCP shifts retirement math away from LLM hallucinations into deterministic code. Your agent calls `calculate_milestones` and receives structured data detailing the exact years required to reach financial independence. Pydantic AI validates every single field in the response at runtime. If the API returns a string instead of a float for the portfolio value, the agent fails loudly before showing bad math to the user.

Validate Lifestyle Costs with Pydantic AI

Users define their target annual spend to see the required nest egg. The `get_lifestyle_targets` operation calculates the exact capital needed based on safe withdrawal rates. You run this logic across any provider. The framework ensures the prompt and the tool output maintain perfect type alignment whether you use a local model or a cloud API.

Model Savings Rate Changes

You need exact dates when adjusting monthly investments. The `simulate_contribution_impact` tool returns the precise timeline shift caused by adding an extra five hundred dollars a month to the portfolio. The framework catches any malformed inputs before they reach the external service. Your application prioritizes correctness over speed when dealing with life-altering financial decisions.

Setup guide

Set up FIRE 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-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 Calculator tools.",
)

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FIRE Calculator. 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 FIRE Calculator MCP in Pydantic AI

Install the slim package with the MCP extra. Create an MCPToolset pointing to your Vinkius HTTP URL. Attach this toolset directly to your agent configuration.

Standard tool calling relies on the model to parse the JSON correctly. This framework forces the output into validated Python objects, eliminating silent data corruption.

Yes. You use the unified MCPToolset class rather than the deprecated HTTP classes. It handles the connection to the external Vinkius host perfectly.

The setup is completely model-agnostic. You point the framework at your local inference engine and it routes the tool calls to the remote calculator.

The FIRE Calculator processes expected annual expenses and withdrawal rates. Vinkius encrypts the payload in transit. The stateless architecture guarantees no financial targets are stored after the calculation finishes.

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