How to Use the ROAS Calculator MCP in Pydantic AI

Q: How do I use the ROAS Calculator with Pydantic AI?

Your agent uses calculateroas by passing in media spend and source revenue. Because of Pydantic AI's validation, you know the output structure is correct every time.

Q: Can the ROAS Calculator help me plan spending with Pydantic AI?

Yes. Running calculatebreakevenroas establishes your minimum required return on ad spend to avoid losing money. It gives you a reliable, validated financial floor.

Q: What is the target ROAS benchmark using the ROAS Calculator with Pydantic AI?

The gettargetbenchmark tool fetches industry standards. This allows your agent to compare its findings against a known, expected metric set.

Generate validated ROAS reports with Pydantic AI's type safety.

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Connect ROAS Calculator MCP to Pydantic AI

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Setup ROAS Calculator with Pydantic AI

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Calculate ad performance metrics.

When you run `calculate_roas`, the output is guaranteed to match a specific schema. Just provide media spend and source revenue, and your agent gets a perfectly validated ROAS number. This type safety means no silent failures; if the API spits out bad data, Pydantic AI fails loud with an error.

Determine financial guardrails for Pydantic AI.

Need to know where the money can go? The `get_target_benchmark` tool pulls industry ROAS goals, giving your agent a validated target. Combining this with `calculate_break_even_roas` gives you precise safety limits. This combination ensures that every spending recommendation is mathematically sound and adheres to established financial boundaries.

Build guaranteed reports using the MCP Server.

Your agent can run a full ad audit. It first checks industry goals with `get_target_benchmark`, then determines the safe budget floor using `calculate_break_even_roas`. Finally, it calculates current performance via `calculate_roas`—and every single result is type-checked and guaranteed correct. This level of data integrity makes your Pydantic AI workflows highly reliable for production use.

Setup guide

Set up ROAS 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": {
        "roas-calculator-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about ROAS Calculator MCP in Pydantic AI

Your agent uses `calculate_roas` by passing in media spend and source revenue. Because of Pydantic AI's validation, you know the output structure is correct every time.

Yes. Running `calculate_break_even_roas` establishes your minimum required return on ad spend to avoid losing money. It gives you a reliable, validated financial floor.

The `get_target_benchmark` tool fetches industry standards. This allows your agent to compare its findings against a known, expected metric set.

It's designed for it. Since every response is validated via Pydantic models, you eliminate hallucinated fields or unexpected data types, making complex modeling safe.

This MCP processes financial metrics like media spend, source revenue, and contribution margins. All inputs are validated to ensure correct numerical processing.

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