TripGo MCP Server for Pydantic AI 9 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect TripGo through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to TripGo "
"(9 tools)."
),
)
result = await agent.run(
"What tools are available in TripGo?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About TripGo MCP Server
What you can do
Connect AI agents to the TripGo platform for intelligent multimodal journey planning:
Pydantic AI validates every TripGo tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- Plan trips combining bus, train, subway, tram, ferry, walking, and cycling
- Find nearby transit stops by GPS coordinates with distance and route info
- Search stops by name or address for precise location discovery
- Get real-time departures and arrivals with live delay estimates
- Track vehicle positions on the map with real-time GPS data
- Review route information including all stops and agency details
- Check stop details with accessibility and amenity information
- Access global regions covering major cities worldwide
The TripGo MCP Server exposes 9 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect TripGo to Pydantic AI via MCP
Follow these steps to integrate the TripGo MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from TripGo with type-safe schemas
Why Use Pydantic AI with the TripGo MCP Server
Pydantic AI provides unique advantages when paired with TripGo through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your TripGo integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your TripGo connection logic from agent behavior for testable, maintainable code
TripGo + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the TripGo MCP Server delivers measurable value.
Type-safe data pipelines: query TripGo with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple TripGo tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query TripGo and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock TripGo responses and write comprehensive agent tests
TripGo MCP Tools for Pydantic AI (9)
These 9 tools become available when you connect TripGo to Pydantic AI via MCP:
get_arrivals
Returns route names, origins, scheduled vs estimated arrival times, and delays. Use this to track incoming vehicles. Requires stop ID. Get upcoming arrivals to a transit stop
get_departures
Returns route names, destinations, scheduled vs estimated departure times, and delays. Use this to check when your next ride arrives. Requires stop ID. Get upcoming departures from a transit stop
get_nearby_stops
Returns stop IDs, names, coordinates, routes serving each stop, and distance from search point. Use this to find nearest transit options before planning trips. Find transit stops near a GPS coordinate
get_regions
Each region has an ID, name, and coverage area. Use this first to verify your city is covered before planning trips. Supports major cities across North America, Europe, Australia, and Asia. List all available transit regions supported by TripGo
get_route_info
Requires route ID. Use this to understand route coverage before planning trips. Get information about a specific transit route
get_stop_details
Requires stop ID from nearby stops or search results. Use this to review stop facilities before waiting there. Get detailed information about a specific transit stop
get_vehicle_positions
Optionally filter by route ID. Use this for real-time tracking of vehicles on the map. Get real-time vehicle positions for transit vehicles
plan_trip
Combines public transport (bus, train, subway, tram, ferry) with walking and cycling. Returns multiple trip options with departure/arrival times, duration, number of transfers, and step-by-step instructions. Optionally specify travel time and preferred transport modes. Plan a multimodal trip between two coordinates
search_stops
g., "Times Square", "Main St & 5th Ave"). Returns matching stops with IDs, names, coordinates, routes, and relevance scores. Use this when you know the stop name or intersection but not exact coordinates. Search for transit stops by name or address
Example Prompts for TripGo in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with TripGo immediately.
"Plan a trip from Central Station to Opera House using only public transit and walking"
"What buses are departing from Stop 12345 in the next 15 minutes?"
"Show me all train and bus vehicles currently running on Route 480"
Troubleshooting TripGo MCP Server with Pydantic AI
Common issues when connecting TripGo to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiTripGo + Pydantic AI FAQ
Common questions about integrating TripGo MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect TripGo with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect TripGo to Pydantic AI
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.