How to Use the API Ninjas MCP in Pydantic AI

Get type-safe fitness data for Pydantic AI with API Ninjas for total request validation.

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Connect API Ninjas MCP to Pydantic AI

Create your Vinkius account to connect API Ninjas 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 API Ninjas with Pydantic AI

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Validate health responses in Pydantic AI

Every response from `get_nutrition` or `get_calories_burned` is validated against your Pydantic models. If the server returns malformed data, your agent errors out immediately rather than guessing. This setup ensures your Pydantic AI agent maintains high data integrity. It's the only way to handle sensitive health calculations reliably.

Calculate metabolic rates in Pydantic AI

Use `get_bmr` and `get_tdee` to feed your agent reliable caloric baselines. Because it's a type-safe environment, you can be certain the output matches your expected schema. Your agent logic becomes cleaner when you don't have to write custom validation for every API call. Pydantic AI handles the contract between the server and your code.

Search and filter exercises in Pydantic AI

Retrieve exercise lists using `get_exercises` and ensure every field—like muscle group or difficulty—is verified at runtime. No hallucinated instructions or missing fields will break your application logic. This MCP Server provides the structure while your agent handles the decision-making. It makes for a predictable, robust pipeline that catches errors before they propagate.

Setup guide

Set up API Ninjas 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": {
        "api-ninjas-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about API Ninjas MCP in Pydantic AI

Install the slim version of the package. Initialize an MCPToolset with your server URL and pass it to your Agent's toolsets list.

Yes, Pydantic AI validates every response against your models at runtime. You get an error if the server output doesn't fit your schema.

It supports both Streamable HTTP and SSE. You can choose the one that fits your deployment architecture best.

Because the framework validates at runtime, you'll catch schema mismatches instantly. It prevents silent failures in your health data processing.

Your health metrics are treated as transient data within the V8-isolated sandbox. We enforce strict memory isolation so your inputs, like waist or neck measurements, are never persisted.

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