How to Use the Agify MCP in Pydantic AI

Enforce strict demographic types with Agify and Pydantic AI.

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Connect Agify MCP to Pydantic AI

Create your Vinkius account to connect Agify 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 Agify with Pydantic AI

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Validate age estimates at runtime

The `predict_age` tool fetches demographic data based on a first name. Pydantic AI validates the incoming integer and optional country string against your defined schemas before the agent ever sees the data. If the API returns a null value or a malformed string, the framework throws a validation error immediately. Silent failures ruin data pipelines. Forcing every prediction through strict runtime checks guarantees your downstream logic always receives valid integers.

Bulk process names with Agify MCP Server

Sending an array of names to the `predict_age_batch` tool returns a corresponding array of estimates. Your agent handles the iteration, while the framework ensures the response list matches the length and type of the input list. This prevents index misalignment when mapping ages back to users. Model-agnostic design means you can swap LLMs without changing the integration. You can run a cloud provider today and a local model tomorrow while keeping the exact same typed execution.

Unified toolset initialization

Connecting the endpoint relies on the modern `MCPToolset` class. You pass the Vinkius HTTP URL directly to the constructor, and the framework handles the transport layer. The older server-specific classes are deprecated. We maintain the infrastructure so you skip the deployment phase. Your agent just connects to the URL and starts validating demographic data against your Python models.

Setup guide

Set up Agify 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": {
        "agify-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Agify MCP in Pydantic AI

Install the slim package with MCP extras. Initialize the toolset with your Vinkius URL and add it to the arrays on your Agent.

The framework fails loudly. Runtime validation catches the type mismatch and throws an error before the agent can hallucinate a fix.

The batch prediction tool accepts lists of strings. Your agent sends the entire array in one request to bypass rate limits.

The protocol provides the base schemas automatically. The framework converts these into Pydantic models for strict runtime enforcement.

Only the first name strings and optional country codes leave your environment. Vinkius isolates the transaction in an ephemeral sandbox and purges the memory immediately after routing the response.

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