How to Use the EZO Asset Intelligence MCP in Pydantic AI

Build type-safe inventory agents using Pydantic AI and EZO Asset Intelligence.

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Connect EZO Asset Intelligence MCP to Pydantic AI

Create your Vinkius account to connect EZO Asset Intelligence 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 EZO Asset Intelligence with Pydantic AI

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Type-safe asset data in Pydantic AI

Every response from the `get_asset_detailed_data` tool is validated against your Pydantic models. If the EZO API returns unexpected fields, your agent fails immediately to prevent data corruption. This approach forces strict adherence to your schema. You get predictable behavior even when your inventory data changes unexpectedly.

Correctness-first inventory checks

Your agents use `list_available_assets` to determine what can be checked out. Because Pydantic AI validates these results, you never work with malformed data. This protects your agent from hallucinations regarding asset status. It ensures that every item identified as available actually exists in your EZO database.

Robust checkout audits

Run `list_currently_checked_out_assets` to reconcile your records. Your agent validates the output against your defined models, ensuring high-fidelity reporting. This makes your inventory audits reliable. You get a clear, validated view of which members have which assets at any given time.

Setup guide

Set up EZO Asset Intelligence 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": {
        "ezo-asset-intelligence-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent EZO Asset Intelligence transactions")
print(result.output)

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Common questions about EZO Asset Intelligence MCP in Pydantic AI

Pydantic AI validates every piece of data returned by the server. This prevents errors by ensuring the tool output matches your expected data structure.

Yes. You instantiate the MCPToolset with your server URL and add it to your Agent. It handles the communication and validation automatically.

If the API returns data that violates your Pydantic model, the agent raises a validation error. This prevents your system from acting on incorrect asset information.

Yes, Pydantic AI is model-agnostic. You can pair it with local models or cloud-hosted ones, as long as the tool output satisfies your validation models.

The server processes your requests using your provided API token. All data from tools like `get_ezo_account_metadata` is handled in-memory and never stored on our platform.

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