How to Use the Dashdoc MCP in AutoGen

Q: How do I give my AutoGen agents access to Dashdoc tools?

Install the autogen-extmcp package and use the mcpservertools function with your Vinkius endpoint URL. This returns a list of tools you can pass directly to your AssistantAgent constructor. The adapter handles converting the tool schemas for you.

Let your AutoGen agents debate, plan, and execute logistics decisions using your live Dashdoc data.

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Connect Dashdoc MCP to AutoGen

Create your Vinkius account to connect Dashdoc to AutoGen 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 Dashdoc with AutoGen

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Enable agent conversations about transports

Give your agents the tools to have productive arguments about logistics. A 'Planner' agent can propose a new shipment. A 'Validator' agent can then call `get_transport_details` to check the cargo or use `search_transports_by_reference` to see if it's a duplicate. They converse, challenge each other's findings, and reach a consensus before taking action. This isn't a simple script; it's a team of specialists using Dashdoc data to make a group decision. One agent might even use `get_my_user_info` to check its own permissions before suggesting a change.

Assign fleet management roles to different agents

Set up a multi-agent system to manage your fleet. A 'Dispatch' agent can use `list_fleet_trucks` and `list_fleet_trailers` to find available vehicles. A 'Compliance' agent can then check their status or specs to ensure they're right for the job. If they disagree—maybe the truck is available but due for service—they can debate the trade-offs. This conversational approach helps catch issues that a single agent or a simple workflow might miss. They use the Dashdoc tool outputs as facts to support their arguments.

Debate adding new addresses and contacts

Use an agent conversation to vet new information before it enters your system. When a new delivery site is needed, one agent can propose creating it. A 'Security' agent can first call `list_saved_addresses` to check if a similar site exists or `list_transport_contacts` to vet the associated partner. Only after the agents agree does one of them get the green light to execute the `create_new_address` tool. This MCP server provides the functions for your AutoGen team to talk through changes, not just blindly execute them.

Setup guide

Set up Dashdoc 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 Dashdoc 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="Dashdoc_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Dashdoc_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Dashdoc. 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 Dashdoc MCP in AutoGen

Install the `autogen-ext[mcp]` package and use the `mcp_server_tools` function with your Vinkius endpoint URL. This returns a list of tools you can pass directly to your `AssistantAgent` constructor. The adapter handles converting the tool schemas for you.

Yes, that's a perfect use case. You'd have a 'Searcher' agent that can use `search_transports_by_reference`. It passes its findings in the conversation to an 'Analyst' agent, which then uses `get_transport_details` to pull the full record and report back.

You configure which tools are available to each agent in your group chat. The LLM then directs the conversation, and an agent with the right tool, like `list_fleet_drivers`, will execute it when it believes it has enough information to contribute.

It introduces checks and balances. Instead of one agent making a mistake, a 'Reviewer' agent can question the output of a tool call made by a 'Worker' agent, leading to more reliable outcomes. It models how human teams collaborate and solve problems.

The data, such as transport order details or contact information, is shared within the AutoGen conversation context. The underlying API calls to the Dashdoc MCP server are handled by Vinkius in a secure, isolated sandbox, protecting the data at the transport layer.

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