How to Use the Coassemble MCP in AutoGen

Q: Can AutoGen resolve student enrollment conflicts in Coassemble?

Your agent can query listcoassembleenrolments to check if a student is already registered. This resolves duplicates through multi-agent debate.

Let your AutoGen agents debate and manage Coassemble student workflows automatically.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Coassemble MCP to AutoGen

Create your Vinkius account to connect Coassemble 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.

GDPR Free for Subscribers

Setup Coassemble with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Coordinate enrollment workflows with this MCP Server

The `enroll_member_in_course` tool acts as the execution mechanism for your multi-agent conversation. An admin agent can propose an enrollment, while a validator agent checks `get_member_group_associations` before confirming. This prevents incorrect course assignments. By forcing your agents to agree on student prerequisites before calling the API, you eliminate enrollment errors completely.

Audit classroom progress using AutoGen agents

The `get_training_completion_results` tool provides raw performance data for your reporting agents to analyze. One agent can flag low scores, while another drafts personalized study recommendations based on the results. This collaborative loop ensures that student tracking is thorough. You get deep analysis and automated intervention plans without writing complex conditional logic.

Automate group assignments via AutoGen

The `list_coassemble_groups` tool lets your coordination agent view all active cohorts in your system. If a new member is found via `list_coassemble_members`, the agent initiates a discussion on where to place them. The agents negotiate based on group capacity and member profiles. Once they reach a consensus, they execute the assignment, keeping your workspace structured.

Setup guide

Set up Coassemble 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 Coassemble 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="Coassemble_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Coassemble 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 Coassemble. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Coassemble now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Coassemble MCP in AutoGen

You pass the MCP tools list to your AssistantAgent constructor. This gives the agent access to tools like `list_coassemble_courses` during conversations.

Your agent can query `list_coassemble_enrolments` to check if a student is already registered. This resolves duplicates through multi-agent debate.

Yes. The adapter supports Streamable HTTP transports, letting you host your agents and your MCP instances in different environments.

If a tool call fails, the error message is returned directly to the agent conversation. The agents then discuss the error and attempt to fix the parameters.

Your LMS data, including grades and user emails, is protected by zero-trust architecture. Vinkius manages the API keys securely, so your AutoGen agents never see the raw credentials.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript