How to Use the Financial Ratios Calculator MCP in Pydantic AI

Force strict type validation on every financial calculation using Pydantic AI.

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Connect Financial Ratios Calculator MCP to Pydantic AI

Create your Vinkius account to connect Financial Ratios 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 Financial Ratios Calculator with Pydantic AI

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Claude

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Fail loudly on bad accounting data

This MCP executes deterministic financial math outside of your language model. Language models hallucinate numbers. When you calculate financial health, a silent error is a disaster. Pydantic AI solves this by validating every single output against your defined schemas. If the agent tries to return a string for a debt multiple, the framework throws a validation error immediately. You catch the bad data at runtime instead of passing corrupted numbers to your investors.

Run exact math with your MCP

You connect these tools using the MCPToolset class and an HTTP transport. The agent reads your raw financial statements and prepares the inputs. It calls `get_short_term_liquidity_ratios` to evaluate working capital. Then it fires `get_long_term_solvency_ratios` to check the capital structure. The external execution ensures the math is deterministic and correct.

Swap models without breaking Pydantic AI

You might start with a local model for testing and switch to a commercial provider for production. The framework is completely model-agnostic. Your tool integrations stay exactly the same. Your chosen model invokes `get_interest_coverage_ratio` to check if EBIT covers the interest expense. The framework validates the returned float. You get guaranteed structure regardless of which LLM powers the backend.

Setup guide

Set up Financial Ratios 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": {
        "financial-ratios-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Financial Ratios Calculator MCP in Pydantic AI

Install the pydantic-ai-slim package with the MCP extra. Initialize an MCPToolset with your Vinkius HTTP endpoint and pass it to the toolsets list on your Agent.

Yes. Every response from the MCP is checked against your Pydantic models. If the data types do not match, the system raises an exception.

You absolutely can. The framework is model-agnostic. You can run local models or commercial APIs while using the exact same toolset configuration.

You get access to over 4,000 managed MCPs. You configure your client once and instantly unlock the entire catalog for your agents.

No. The system processes your liability and asset figures in memory to perform the division. It drops the data immediately after returning the strict typed response.

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