How to Use the Craftboxx MCP in Pydantic AI

Build type-safe Craftboxx agents with Pydantic AI to ensure every API interaction meets your exact data requirements.

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Connect Craftboxx MCP to Pydantic AI

Create your Vinkius account to connect Craftboxx 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 Craftboxx with Pydantic AI

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Perplexity

Strict Pydantic AI schema validation

Every response from `list_customers` or `list_orders` is automatically validated against Pydantic models. If the API structure changes, your agent throws an error instead of hallucinating values. This prevents silent data corruption. You know exactly what data your agent is working with at every step of the process.

Robust tool execution

Use the `MCPToolset` to integrate Craftboxx into your agent's logic. The framework handles the communication, while your typed models enforce the data integrity. You avoid the common pitfalls of loose JSON handling. Every field is defined, checked, and ready for your business logic.

Operational visibility

Call `list_tasks` and `get_business_stats` to get a snapshot of your current operations. Your agent validates these snapshots to ensure the data is accurate before taking any action. This gives you a reliable way to audit your agent's decisions. You see exactly what the agent sees, verified by your own type definitions.

Setup guide

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

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

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

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Common questions about Craftboxx MCP in Pydantic AI

Install the slim MCP package and use the MCPToolset class. Pass the resulting toolset to your agent to enable type-safe interaction with Craftboxx.

Your agent will fail loudly with a validation error. This is intentional to prevent your system from acting on incorrect or malformed information.

Yes. Pydantic AI is model-agnostic, meaning you can connect any model—local or hosted—to these tools. The validation remains strict regardless of the underlying model.

It does. You can use SSE or Streamable HTTP transports to connect the server. Ensure your server is reachable, and the framework handles the rest.

We enforce strict schema validation for every request, which acts as a secondary layer of data integrity. Your sensitive customer and order records are handled strictly through authenticated, transient calls.

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