How to Use the TollGuru MCP in AutoGen

Q: Does this MCP Server support monitoring usage with TollGuru Alternative and AutoGen?

The gettransactioncount tool lets your agents monitor the total API transaction count. This is vital for building accountability into multi-agent financial planning.

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Compare Routes Using the MCP Server

Agents can use `calculate_tolls_by_coordinates` to generate multiple route options and their respective toll breakdowns. You can set up a debate where one agent recommends the fastest path, while another prioritizes minimizing tolls. This negotiation process converges on the optimal plan based on competing inputs from the MCP Server's comprehensive data.

Reconcile Trips with AutoGen Agents

A multi-agent system can use `calculate_tolls_by_gps` to analyze raw trip data. One agent handles the initial processing, and another critiques the results for accuracy or missing segments. This allows your agents to debate the best cost estimate after analyzing complex post-trip reconciliation data.

Verify Toll Rates Before Planning

Instead of calculating a route immediately, an agent can first call `get_toll_rates` for known checkpoints. This pre-check capability lets the agents debate whether a planned stop is even worth the cost. It provides a critical decision point where consensus must be reached on feasibility before committing to a full itinerary.

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

TollGuru Alternative feeds verifiable, multi-option cost data into your agent debate. Agents don't guess the price; they negotiate based on concrete inputs from `calculate_tolls_by_address`.

Yes. You can build a debate where one agent uses coordinates to calculate routes, and another challenges that route using the polyline tool (`calculate_tolls_by_polyline`) to find potential cost savings.

The `get_transaction_count` tool lets your agents monitor the total API transaction count. This is vital for building accountability into multi-agent financial planning.

You can set up a debate between two specialized agents: one optimizing for cost (using address inputs) and another optimizing for speed, forcing consensus on the best trade-off.

This server handles global toll intelligence, processing inputs like encoded polyline strings, geographical coordinates, and vehicle type specifications for cost calculation.

Use it with your favorite AI tools

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