How to Use the Ably MCP in AutoGen

Q: How do I configure Ably tools in AutoGen?

Install autogen-extmcp. Pass your Vinkius endpoint to mcpservertools(), and hand the resulting list directly to your AssistantAgent constructor.

Q: Can AutoGen agents read Ably channel history?

One agent calls getmessages to pull the timeline. It shares those logs with the conversation group so other agents can analyze the context.

Q: Do AutoGen agents know which Ably channels to monitor?

The system maps the environment first. An agent executes listchannels and getchannelmetadata to build a directory of active rooms before assigning monitoring tasks.

Give your AutoGen agents the ability to debate, coordinate, and publish messages to Ably channels.

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Connect Ably MCP to AutoGen

Create your Vinkius account to connect Ably 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 Ably with AutoGen

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Perplexity

Multi-agent debate over Ably broadcasts

Complex notifications require consensus. A copywriter agent drafts a broadcast, while a compliance agent reviews the text before anyone calls `batch_publish` to send it out. They argue over the exact wording. Once both agents agree the payload meets your guidelines, the execution agent fires `publish_message` to push the final text to the designated channel.

Coordinate presence-based actions

Your team of agents monitors who is currently online. The tracking agent runs `get_presence` to see active members, sharing that state with the rest of the AutoGen swarm. Routing decisions happen automatically. If the target user is offline, the routing agent overrides the chat attempt and triggers `push_publish` instead to hit their mobile device.

Manage MCP Server tokens autonomously

Security agents handle credential rotation without human input. By monitoring application limits via `get_stats`, an admin agent decides when it is time to cycle access. Execution is immediate and logged. The agent invokes `revoke_tokens` to kill old sessions, forcing clients to re-authenticate while the rest of the swarm continues monitoring `list_channels` for new activity.

Setup guide

Set up Ably 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 Ably 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="Ably_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install `autogen-ext[mcp]`. Pass your Vinkius endpoint to `mcp_server_tools()`, and hand the resulting list directly to your AssistantAgent constructor.

One agent calls `get_messages` to pull the timeline. It shares those logs with the conversation group so other agents can analyze the context.

The system maps the environment first. An agent executes `list_channels` and `get_channel_metadata` to build a directory of active rooms before assigning monitoring tasks.

They coordinate the target list internally. Once they agree on the audience, an agent calls `batch_push_publish` to fire the alerts in one shot.

The platform isolates every request. Usage statistics and message serials traverse a strictly stateless V8 sandbox, ensuring your private communication data never persists outside the specific tool call.

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