How to Use the 99Minutos Express MCP in Pydantic AI

Type-safe logistics automation for Pydantic AI agents using 99Minutos Express.

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Connect 99Minutos Express MCP to Pydantic AI

Create your Vinkius account to connect 99Minutos Express 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 99Minutos Express with Pydantic AI

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Perplexity

Strict type validation for 99Minutos Express

Your agent uses `get_rates` with strict Pydantic models to ensure every price quote matches expected data types. If the API returns garbage, your agent rejects it immediately. Run `list_coverage` to validate ZIP codes against your business rules. The framework stops any invalid shipment requests before they hit the courier network.

Safe order handling in Pydantic AI

Trigger `create_order` and verify the response structure against your defined schemas. You never deal with silent corruption in your delivery data. Use `get_order` to pull specific details into a typed object. Your agent works with clean, predictable data every time it talks to the delivery server.

Courier state management in Pydantic AI

List your active runs with `list_orders` to keep your internal state aligned with physical reality. The agent keeps your delivery queue orderly. Check `list_return_orders` to ensure reverse logistics follow your strict validation rules. You maintain total control over your shipment lifecycle.

Setup guide

Set up 99Minutos Express 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": {
        "99minutos-express-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to 99Minutos Express tools.",
)

result = await agent.run("List recent 99Minutos Express transactions")
print(result.output)

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Common questions about 99Minutos Express MCP in Pydantic AI

Instantiate an MCPToolset pointing to the server endpoint. Your agent will then validate all tool inputs and outputs against your Pydantic schemas.

The framework forces the agent to adhere to your strict data models. If a tool response is malformed, the system triggers a validation error instead of guessing.

Yes, the toolset is model-agnostic. You can switch between different LLMs while your validation logic remains consistent for all delivery calls.

Your delivery parameters are validated in memory and discarded after the transaction. We maintain strict boundaries between your data and the server.

The server sends order statuses, coverage zones, and rate objects. Everything is validated against your Pydantic classes to prevent runtime errors.

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