How to Use the DigitalChalk MCP in AutoGen

Enable multi-agent debate for DigitalChalk operations using AutoGen's conversational framework.

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Connect DigitalChalk MCP to AutoGen

Create your Vinkius account to connect DigitalChalk 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 DigitalChalk with AutoGen

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Run multi-agent debates on LMS management

Assign one agent to `list_high_performing_learners` and another to `get_offering_details`. They discuss the findings to decide if a course needs adjustment. This conversation-based approach surfaces insights that a single agent might miss. It forces the system to consider conflicting metrics before taking action.

Negotiate administrative tasks between agents

Use the `list_lms_users` tool to feed a security agent and a reporting agent. They debate access rights based on the user identity returned by the server. AutoGen agents check each other's work. You set the goals and watch the agents negotiate the best way to handle the data.

Automate complex workflows with the MCP Server

Your agents call `list_user_enrollments` to gather facts for a final decision. The system converges on a result only after the agents have challenged the initial data. It handles the heavy lifting of reasoning through your training data. You get a consensus-driven output for every request.

Setup guide

Set up DigitalChalk 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 DigitalChalk 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="DigitalChalk_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You pass the tool list from the server to your AssistantAgent. The adapter handles the schema mapping so your agents can call the tools immediately.

They can. If one agent finds a discrepancy in the learner profile, it can challenge another agent to clarify the data before proceeding.

Just the standard endpoint configuration. You use the streamable HTTP transport to connect the server, and the agents handle the rest via conversation.

You see the entire conversation in your logs. Every time an agent decides to call a tool, the request and response are recorded in the transcript.

Data is processed only by the agents you define. Your credentials are used exclusively to sign requests, keeping the interaction local and secure.

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