How to Use the Dashly MCP in AutoGen

Q: How do my AutoGen agents access the Dashly tools?

After installing the extension, you use the mcpservertools function with your server URL. This returns a list of tool-proxy objects that you pass to your AssistantAgent when you create it.

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Connect Dashly MCP to AutoGen

Create your Vinkius account to connect Dashly 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 Dashly with AutoGen

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Assemble a Team of Support Agents

This server gives your AutoGen agents the tools to work together on support issues. You can create a 'TriageAgent' that uses `list_conversations` to find new tickets and a 'SupportAgent' that uses `get_conversation` to investigate them. The agents debate the best approach in a group chat. The TriageAgent might propose an action, and the SupportAgent can refine it before anyone uses `send_reply` to talk to the customer.

Debate User Actions Before Committing

Prevent mistakes by making agents justify their actions. One agent might want to update a user with `set_user_props`, but another agent can act as a reviewer, checking the user's history first with `get_user_details`. This conversational approach is AutoGen's specialty. The final action is a result of consensus, not a single agent's impulse. It's perfect for critical tasks like updating user data or sending automated replies.

Connect AutoGen to Dashly with This MCP Server

Getting started is straightforward. The `autogen-ext[mcp]` package provides a helper function to fetch all available Dashly tools from your Vinkius endpoint. Just pass the list of tools into your `AssistantAgent` constructor. The `McpToolAdapter` handles converting the tool schemas automatically, so your agents can start using them right away.

Setup guide

Set up Dashly 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 Dashly 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="Dashly_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Dashly 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 Dashly. 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 Dashly MCP in AutoGen

After installing the extension, you use the `mcp_server_tools` function with your server URL. This returns a list of tool-proxy objects that you pass to your `AssistantAgent` when you create it.

Yes. You fetch the full tool list from the MCP server, then filter it in your Python code. Create one list for your 'support' agent and a different, more restricted list for a 'reviewer' agent.

A classic one is a support-and-sales team. A support agent handles a user's issue with `get_conversation`, while a sales agent listens in, checks `get_user_details`, and uses `track_event` to flag them as a potential lead.

Through conversation. You define the rules of engagement for the group chat. An agent might propose a reply with `send_reply`, but it may require another agent to approve it before it's actually executed.

The data, including user records and conversation text, flows from Dashly through a secure Vinkius sandbox directly to your running AutoGen agents. The interactions happen within your agent environment; Vinkius provides only the secure, ephemeral tool execution layer.

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