How to Use the Loomly MCP in AutoGen

Debate your social media strategy using AutoGen and Loomly, letting agents negotiate approvals before content goes live.

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Connect Loomly MCP to AutoGen

Create your Vinkius account to connect Loomly 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 Loomly with AutoGen

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Perplexity

Debate Loomly posts using AutoGen agents

Assign one agent to draft content with `create_new_post` and another to critique it using `get_post_details`. They argue over tone and accuracy until they reach a consensus. This adds a layer of human-like oversight to your automation. You aren't just firing off posts; you are building a system that challenges its own assumptions before anything reaches your calendar.

Negotiate calendar access in AutoGen

Use a security agent to check `get_calendar_details` before allowing a content agent to proceed. This ensures that only authorized calendars are targeted for new posts. This prevents accidental changes in sensitive environments. The agents negotiate based on the metadata retrieved, stopping the process if the parameters don't align with your requirements.

Analyze performance with AutoGen agents

Have your agents compare `list_post_interactions` data against previous engagement goals. They can debate whether a post's performance warrants a follow-up or a change in strategy. This loop keeps your content relevant. By letting agents argue over the metrics, you uncover insights that a single model would likely miss in isolation.

Setup guide

Set up Loomly 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 Loomly 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="Loomly_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Loomly data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Loomly MCP in AutoGen

Pass the MCP tools to your AssistantAgent constructor. This allows your agents to share access to the Loomly API and debate their findings in real-time.

That is the point. You can configure one agent to prioritize speed and another to prioritize brand safety, forcing them to negotiate until they find a middle ground.

The MCP server uses a zero-trust model to gate access to your data. Only the agents you authorize can invoke tools like `list_calendar_posts`, ensuring your private content remains strictly internal.

Yes, the conversation history logs every tool call and response. You can review the debate logs to see exactly how your agents used the Loomly tools to make their final decisions.

You can filter the tool list before passing it to the agent. This restricts the agents to only the operations you deem necessary for their specific roles, like limiting them to read-only access.

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