How to Use the Mention MCP in AutoGen

Deploy AutoGen agent teams that debate, triage, and act on live brand mentions in real time.

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Connect Mention MCP to AutoGen

Create your Vinkius account to connect Mention 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 Mention with AutoGen

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Coordinate Brand Triage with AutoGen Agents

The `list_recent_mentions` tool lets your triage agent pull the latest web hits into a group chat. From there, a sentiment agent analyzes the text, while a PR agent decides whether to flag the item for immediate response. If the group agrees a post is critical, the triage agent runs `favorite_mention` to bookmark it. By delegating tasks across specialized agents, you avoid false positives and ensure only high-priority brand discussions get escalated.

Let Agents Manage Your MCP Server Alerts

The `create_monitoring_alert` tool allows your supervisor agent to establish new tracking rules based on chat discussions. If a security agent spots a potential brand crisis, it instructs the supervisor to spin up a new alert immediately. Conversely, when a project ends, the supervisor agent uses `remove_monitoring_alert` to clean up the workspace. This automated housekeeping keeps your active alert count low and prevents your agents from getting distracted by stale data.

Run Consensus Audits on Reach Metrics

The `get_alert_statistics` tool provides raw volume and reach numbers to your analytical agents. A data analyst agent pulls these metrics and debates with a marketing agent about whether a sudden spike represents a real trend or just noise. To dig deeper, the agents call `get_alert_details` to inspect the exact parameters of the active tracker. This collaborative analysis happens entirely within your AutoGen conversation loop, producing clear summaries without human intervention.

Setup guide

Set up Mention 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 Mention 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="Mention_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Use the AutoGen MCP extension to wrap the server tools. Pass the resulting tool list directly to your AssistantAgent's constructor so it can call them during group chats.

Yes. The server handles concurrent requests. Your agents can call `list_recent_mentions` and debate the findings in a shared conversational thread without blocking each other.

The AutoGen MCP adapter automatically maps the server tool schemas to the format the agents expect. Your agents can call `search_mentions_by_keyword` using standard JSON arguments without manual translation.

It uses the Streamable HTTP transport managed by Vinkius. This allows your multi-agent system to connect securely from any environment without needing local stdio access.

The MCP Server processes all webhook URLs, alert profiles, and mention data inside an ephemeral V8 sandbox. No data is cached or stored on Vinkius, maintaining zero-trust security between your agents and the API.

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