How to Use the Amplenote MCP in AutoGen

Q: How do I connect Amplenote to AutoGen?

Grab the autogen-extmcp package. Use mcpservertools with your Vinkius HTTP endpoint to fetch the tool schemas, then pass that list directly into your AssistantAgent constructor.

Q: What happens if an AutoGen agent deletes an Amplenote task?

The deletenote function is permanent. We recommend assigning deletion capabilities to a dedicated supervisor agent that requires consensus from other agents before executing the command.

Give your AutoGen agents the ability to debate, assign, and track Amplenote tasks autonomously.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Amplenote MCP to AutoGen

Create your Vinkius account to connect Amplenote 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 Amplenote with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-agent Amplenote task delegation

Microsoft's framework shines when multiple agents collaborate on complex problems through the MCP connection. A researcher agent can pull background information using `get_note` while a planner agent simultaneously evaluates the project timeline. Once they agree on a direction, the planner uses `create_task` to assign actionable items. The agents negotiate priorities and due dates before anything gets written to your workspace.

Debate notes via AutoGen MCP Server

Sometimes a single prompt isn't enough to draft good documentation. You can configure a writer agent to draft a proposal via `create_note` and a reviewer agent to critique it immediately. If the reviewer finds flaws, it instructs the writer to execute `update_note` with specific corrections. This iterative debate forces the model to correct its own mistakes before saving the final draft.

Track project consensus

Managing a busy workspace requires constant oversight. Your AutoGen MCP setup can deploy an auditor agent that runs `list_tasks` every morning to identify overdue items across all documents. When it finds lagging work, it alerts a manager agent. They discuss whether to push the deadline back via `update_task` or escalate the issue based on the priority metadata retrieved from the system.

Setup guide

Set up Amplenote 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 Amplenote 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="Amplenote_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Grab the autogen-ext[mcp] package. Use mcp_server_tools with your Vinkius HTTP endpoint to fetch the tool schemas, then pass that list directly into your AssistantAgent constructor.

They can. You just need to provide the note UUID to both agents. They will debate the required changes and take turns calling update_note until they reach a consensus.

The delete_note function is permanent. We recommend assigning deletion capabilities to a dedicated supervisor agent that requires consensus from other agents before executing the command.

Yes, the integration handles both stdio and Streamable HTTP transports natively. The McpToolAdapter converts the Amplenote MCP server schemas into a format the agents understand automatically.

Vinkius isolates every agent interaction inside a secure V8 environment. Your meeting transcripts and project plans are never exposed to shared memory, and the ephemeral container dies the second the debate concludes.

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