How to Use the TripGo MCP in AutoGen

Q: How do I use TripGo with AutoGen to plan a complex journey?

The agents handle it. One agent calls plantrip initially, and then another agent might call getrouteinfo on the resulting route ID for more details. They negotiate until they reach the best final itinerary.

Q: Can AutoGen use TripGo to check available regions?

Yes. You can make one agent run getregions. This output informs a second agent, which then determines if the user's intended location is even supported by the MCP Server.

Q: If I search for stops using TripGo with AutoGen, what happens?

The 'Search Agent' executes searchstops based on the name or address. The output is then passed to another agent which validates the resulting stop IDs before proceeding.

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Setup TripGo with AutoGen

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Perplexity

Simulating Travel Decisions via AutoGen

You can build a system where one agent plans the trip using `plan_trip`. A second 'Risk Agent' can then challenge that plan by calling `get_regions` to ensure coverage. The final decision is only made once both agents agree on viable routes. The result isn't just an answer; it's a consensus derived from multiple API calls.

Debating Transit Status with AutoGen

Set up two agents: one checks real-time arrivals using `get_arrivals`, and the other uses `get_departures`. They debate which stop is better to wait at, challenging each other based on scheduled vs. estimated times. This simulates a complex decision where multiple data points must be weighed against each other.

Multi-Step Location Verification

An 'Input Agent' uses `search_stops` to find coordinates based on an intersection name. A 'Verification Agent' then takes those resulting IDs and runs `get_stop_details`. The final, approved location data is passed back for the ultimate action. This process forces multi-step verification before any trip planning can occur.

Setup guide

Set up TripGo MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes TripGo tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="TripGo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent TripGo data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="TripGo_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent TripGo data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TripGo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about TripGo MCP in AutoGen

The agents handle it. One agent calls `plan_trip` initially, and then another agent might call `get_route_info` on the resulting route ID for more details. They negotiate until they reach the best final itinerary.

Yes. You can make one agent run `get_regions`. This output informs a second agent, which then determines if the user's intended location is even supported by the MCP Server.

This server handles coordinates, route names, stop IDs, and various time measurements (scheduled vs. estimated). The agents use these structured inputs to debate and converge on a single decision.

The 'Search Agent' executes `search_stops` based on the name or address. The output is then passed to another agent which validates the resulting stop IDs before proceeding.

This server touches location coordinates, route names, and stop facility details. By using it through an autonomous multi-agent system, you control exactly which pieces of structured API data are passed between your agents.

Use it with your favorite AI tools

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