How to Use the Countly MCP in AutoGen

Q: How do AutoGen agents talk to the Countly MCP server?

Use the mcpservertools helper to load the tools into your AssistantAgent. The adapter handles the schema so agents can call functions immediately.

Let your AutoGen agents debate and act on real-time product data.

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Connect Countly MCP to AutoGen

Create your Vinkius account to connect Countly 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 Countly with AutoGen

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Debate product stats in AutoGen

Have your performance agent call `read_metrics` while a security agent checks for anomalies. They discuss the data and converge on a decision based on the numbers. This setup allows agents to challenge each other's interpretations of the analytics. You get a consensus-driven view of your product health.

Log agent actions with AutoGen

Use `record_events` so your multi-agent system can log its own progress. If an agent completes a task, it records the event to your analytics dashboard. This keeps a clear audit trail of what the agents did and when. You can look back at the dashboard to see the agents' history of actions.

Manage session state in AutoGen

Start and stop sessions with `begin_session` and `end_session` to delineate agent work blocks. It helps you track how much time agents spend on specific tasks. Use `update_session` if the debate takes longer than expected. Your analytics will show the true time spent on these automated discussions.

Setup guide

Set up Countly 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 Countly 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="Countly_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Countly MCP in AutoGen

Use the mcp_server_tools helper to load the tools into your AssistantAgent. The adapter handles the schema so agents can call functions immediately.

Yes, agents can retrieve data via the MCP server and use it as evidence in their conversation. They weigh the metrics to reach a final decision.

The server is compatible with any agent that supports MCP. You can share the tool list across multiple agents in your group chat.

You choose which agents receive the tools. It is best practice to give only the specialized agents access to read or write analytics data.

The MCP server uses a secure sandbox for all operations. Only the data explicitly returned by the server enters your agent's context window.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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