How to Use the Amplifier MCP in Pydantic AI

Type-safe fulfillment for Pydantic AI. Validate every order and inventory update against your models to prevent silent failures.

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Connect Amplifier MCP to Pydantic AI

Create your Vinkius account to connect Amplifier 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 Amplifier with Pydantic AI

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Type-checked orders for Pydantic AI

Submit orders with total confidence using `create_order`. Because this is Pydantic AI, the tool response is checked against your models at runtime. If the API returns a malformed structure, the agent throws an error immediately. You catch bad data before it hits your database.

Inventory validation in Pydantic AI

Fetch stock levels with `get_inventory_report` and let your agent enforce the schema. It ensures your inventory counts match the types you expect. This prevents the agent from hallucinating fields during `list_items` calls. You get clean, typed data every time the agent performs a check.

Typed tracking for Pydantic AI

Use `get_shipment_report` to pull verified tracking data. Every field is validated against your Pydantic models, ensuring your agent logic stays consistent. It handles order metadata from `get_order_details` with strict type enforcement. Your fulfillment workflow stays predictable and error-free.

Setup guide

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

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

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

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Common questions about Amplifier MCP in Pydantic AI

You initialize an MCPToolset with the server URL and pass it to your Agent instance. The framework handles the conversion of tool responses into your typed models.

It does. When the agent receives a response, Pydantic AI validates the structure. If the tool output doesn't match your defined model, the process stops.

Yes. You can share the same toolset across different agents. Each one will apply its own model validation to the data it receives from the tools.

If a tool fails, it raises an exception that the framework catches. This prevents the agent from hallucinating a valid response based on an empty or corrupt result.

We enforce strict token-based authentication for all connections. Your sensitive fulfillment and shipment records are processed in memory and never persisted in our infrastructure.

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