How to Use the Upper Route Planner MCP in Pydantic AI

Q: How do I check the status of Upper Route Planner using Pydantic AI?

Run checkupperstatus. The response is validated immediately against a simple model, confirming the MCP Server's operational status. This gives you certainty before executing complex tasks.

Q: Can I list active routes using Upper Route Planner with Pydantic AI?

Yes. You use listupperroutes, and because of runtime validation, you get a predictable list structure back every single time. This prevents bad data from entering your pipeline.

Q: Does this MCP Server support multi-driver operations with Pydantic AI?

It does. You first call listupperdrivers to get a list of validated driver records, then pass that structured data into the createupperdeliverytask tool.

Q: How do I retrieve details for an upper route stop using Upper Route Planner and Pydantic AI?

Use getupperroutestop. The result is validated against the model, so you're guaranteed to get specific fields like location coordinates, which are crucial for your agent logic.

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Connect Upper Route Planner MCP to Pydantic AI

Create your Vinkius account to connect Upper Route Planner 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 Upper Route Planner with Pydantic AI

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Scheduling Tasks with the MCP Server

You call `create_upper_delivery_task`. Because everything is validated against Pydantic models at runtime, if the API returns unexpected data, your agent fails loudly. No silent corruption. This means when you schedule a delivery task, you know the input structure is correct—it's rock solid.

Listing Available Drivers

`list_upper_drivers` returns driver data validated by Pydantic. This ensures that every ID and name you get back are correctly typed, which is huge for model-agnostic systems. When your agent processes this list, it won't encounter a field mismatch, because the server enforces the schema.

Getting Stop Details from Upper Route Planner

To get stop details, use `get_upper_stop_details`. The output is always validated against your defined Pydantic model. This guarantees that coordinates and hours are exactly what you expect. This level of type safety means your agent doesn't have to guess the structure of the data it receives.

Setup guide

Set up Upper Route Planner 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": {
        "upper-route-planner-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Upper Route Planner transactions")
print(result.output)

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Common questions about Upper Route Planner MCP in Pydantic AI

Run `check_upper_status`. The response is validated immediately against a simple model, confirming the MCP Server's operational status. This gives you certainty before executing complex tasks.

The server deals with delivery tasks, route stops, and driver profiles. The core benefit here is that the output from these tools always conforms to a defined Python schema.

Yes. You use `list_upper_routes`, and because of runtime validation, you get a predictable list structure back every single time. This prevents bad data from entering your pipeline.

It does. You first call `list_upper_drivers` to get a list of validated driver records, then pass that structured data into the `create_upper_delivery_task` tool.

Use `get_upper_route_stop`. The result is validated against the model, so you're guaranteed to get specific fields like location coordinates, which are crucial for your agent logic.

Use it with your favorite AI tools

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