How to Use the Metricool MCP in AutoGen

Deploy AutoGen agents that debate, plan, and schedule your Metricool social campaigns through consensus.

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Connect Metricool MCP to AutoGen

Create your Vinkius account to connect Metricool 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 Metricool with AutoGen

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Coordinate social scheduling via AutoGen agents

The Metricool MCP server enables your AutoGen planning agent to fetch current queues using `get_social_planner` and present them to a reviewer agent. This multi-agent setup ensures every post is critiqued before it goes live. The reviewer agent checks the queue for tone and timing, while the planner agent uses `list_metricool_profiles` to target the right accounts. They debate changes and only write updates once they reach consensus.

Audit ad performance with specialized agents

The `get_ads_performance` tool provides the raw data for a specialized finance agent to debate campaign efficiency with a creative agent. They analyze return on ad spend without manual intervention. The finance agent flags high costs, while the creative agent looks at engagement metrics. Together, they decide whether to pause campaigns or adjust target audiences based on the live data.

Consolidate multi-channel reports through debate

The `get_unified_summary` MCP tool gives your AutoGen reporting group a single source of truth for cross-channel metrics. A data collector agent pulls this summary and presents it to a strategy agent. Instead of a single agent making assumptions, the strategy agent cross-references the summary with platform-specific data from `get_linkedin_analytics`. They collaborate to write a balanced performance report.

Setup guide

Set up Metricool 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 Metricool 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="Metricool_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Metricool MCP in AutoGen

Use the server tools utility to fetch the tool definitions and pass them to your agent constructor. This lets agents in your group call tools like `list_metricool_profiles` during their conversations.

Yes, a copywriter agent can draft the post, a compliance agent can verify it, and a publisher agent can use `get_social_planner` to check the queue. The team only schedules the post once all agents approve.

The adapter automatically translates the MCP schemas into the format AutoGen expects. This ensures tools like `get_facebook_analytics` receive properly formatted parameters from the calling agent.

The agents continue their conversation, trading insights from tools like `get_unified_summary` until they resolve the discrepancy. You set the maximum rounds of debate to keep the execution efficient.

Your API tokens stay isolated within the Vinkius MCP host, meaning your AutoGen agents only interact with the exposed tools. No raw credentials or sensitive database records are ever shared with the LLM providers.

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