How to Use the TollGuru MCP in AutoGen

Q: Is TollGuru necessary for complex trip budgeting with AutoGen?

Yes, it is. The calculatetollroute function provides the full set of variables (tolls, fuel, time) that opposing agents can argue over. This structured data makes the negotiation meaningful and highly detailed.

Q: Can I use TollGuru with AutoGen for multi-stop optimization?

You can. The calculatetollmultistop function provides the baseline optimal plan, which then serves as a starting point for your debate. Agents can argue over whether to include additional waypoints or adjust the sequence.

Achieve consensus on costs with TollGuru using AutoGen's debate framework.

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Setup TollGuru with AutoGen

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Negotiate Optimal Routes using the MCP Server

Need to know if a route is efficient? Give your agents access to `calculate_toll_route`. You can set up multiple AutoGen agents—a 'Cost Agent' and a 'Speed Agent'—to debate the best path. The Cost Agent runs the function, gets the toll details, and presents the cost; the Speed Agent challenges that based on time value. The final decision is a consensus achieved through deliberation between competing perspectives, giving you a highly vetted route plan.

Debate Multi-Stop Logistics Plans with AutoGen

When dealing with complex multi-stop delivery routes, the `calculate_toll_multi_stop` tool provides initial data. You can then have one agent act as a 'Validator' and another as an 'Optimizer.' They debate whether the current waypoint sequence is truly minimal in cost or if there's a better order—a negotiation process that goes beyond simple calculation. This consensus-driven approach ensures every possible angle, from tolls to fuel costs, is reviewed before the final plan is generated.

Challenge Assumptions with TollGuru’s MCP Server

If you only have a polyline and need cost validation, use `calculate_toll_from_polyline`. You can task one AutoGen agent to run this check, while another is tasked with comparing the output against known maximum budgets. The agents debate if the calculated toll costs are acceptable based on organizational policy. This system doesn't just execute; it forces a discussion around the viability of the results.

Setup guide

Set up TollGuru 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 TollGuru 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="TollGuru_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent TollGuru 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="TollGuru_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Common questions about TollGuru MCP in AutoGen

The MCP Server provides objective, structured data—like exact toll plaza names or fuel costs—that agents can use to challenge or support each other's conclusions. The tool output becomes evidence in the multi-agent debate.

Yes, it is. The `calculate_toll_route` function provides the full set of variables (tolls, fuel, time) that opposing agents can argue over. This structured data makes the negotiation meaningful and highly detailed.

The server manages route geometry (polylines), vehicle specifications, and detailed cost structures including tolls, fuel costs, and time estimates. These metrics drive the deliberation process between agents.

You can. The `calculate_toll_multi_stop` function provides the baseline optimal plan, which then serves as a starting point for your debate. Agents can argue over whether to include additional waypoints or adjust the sequence.

The server touches route geometry (polylines), vehicle specifications, and detailed cost structures. Because these metrics are highly structured, they make excellent evidence for agents debating past performance against budgets.

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