How to Use the TripGo MCP in Pydantic AI

Q: Can I find stops near me using TripGo and the Pydantic AI?

Yes. The getnearbystops tool returns IDs, coordinates, and route lists. Since these are validated, your agent can trust that every coordinate point is usable for further planning.

Q: Does TripGo help with multi-city trip planning via the Pydantic AI?

Yes. You start by running getregions to validate coverage across continents. The resulting region list is strongly typed, making the subsequent calls much safer.

Q: How do I check schedules using TripGo and the Pydantic AI?

You use getdepartures. If the API data for scheduled times or delays is missing a field (like 'delay'), Pydantic will flag it, letting you know exactly what's wrong before your agent fails.

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Connect TripGo MCP to Pydantic AI

Create your Vinkius account to connect TripGo 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 TripGo with Pydantic AI

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Guarantee structured data from stop facilities.

When you call `get_stop_details` using a specific stop ID, the response is validated by Pydantic. This means if the API sends unexpected field names or types, your agent fails loudly—no silent corruption. This guarantees that when your agent builds a user profile or report based on facility data, it's 100% correct.

Find precise transit stop locations by name.

The `search_stops` tool lets you find stops using an intersection or street name. Since Pydantic validates the output structure, your agent always receives a predictable list of matches, IDs, and coordinates. This structured approach is vital because it prevents errors when passing location data to other tools.

Get comprehensive vehicle tracking data.

Use `get_vehicle_positions` for real-time tracking. Because the response models are validated, your agent knows exactly how many vehicles and what their coordinates look like. It's reliable: you get a clean list of positions that can be used immediately in downstream logic.

Setup guide

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

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

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

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Common questions about TripGo MCP in Pydantic AI

Pydantic AI uses MCP Server HTTP endpoints to connect. The key is validation: every tool output, whether it's a trip plan or stop detail, must conform to predefined Pydantic schemas.

Yes. The `get_nearby_stops` tool returns IDs, coordinates, and route lists. Since these are validated, your agent can trust that every coordinate point is usable for further planning.

The server handles location coordinates and stop IDs. The benefit of Pydantic here is ensuring that even if partial or corrupted location data comes back, your agent catches the error instead of misusing it.

Yes. You start by running `get_regions` to validate coverage across continents. The resulting region list is strongly typed, making the subsequent calls much safer.

You use `get_departures`. If the API data for scheduled times or delays is missing a field (like 'delay'), Pydantic will flag it, letting you know exactly what's wrong before your agent fails.

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