How to Use the FullStory MCP in AutoGen

Q: How do AutoGen agents share FullStory data?

Agents pass the JSON output from tools like listsessions through their conversation history. They discuss the data points until they reach a conclusion.

Q: How do I monitor AutoGen tool usage?

Every call to listusers or other tools is recorded in the agent conversation log. You review the transcript to see exactly what evidence the agents used.

Q: How is PII handled when AutoGen uses FullStory?

You implement a policy where the agents must call deleteuser if they process sensitive data that shouldn't persist. The agents follow your instructions to maintain compliance.

Use AutoGen agents to debate and resolve user friction by negotiating FullStory session evidence in real-time.

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Create your Vinkius account to connect FullStory 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 FullStory with AutoGen

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Debate session findings in AutoGen

Assign one agent to `get_session_events` and another to analyze the results. They debate whether the click trails show a genuine bug or just user confusion. This multi-agent approach prevents one-sided interpretations. The agents negotiate based on the evidence they pull from the session recordings, arriving at a consensus before they report back to you.

Verify integration health with AutoGen

Have a security-focused agent run `get_account_info` to check your integration status. It cross-references the workspace scope against your security requirements. If the configuration doesn't match, the agent flags it for a human. This ensures your analytics boundary is always configured correctly without you doing manual checks.

Manage user telemetry via AutoGen

Use `create_event` to inject server-side metrics during a multi-agent discussion. Agents push these events when they discover a new edge case in the system. This keeps your analytics updated with findings from the agents themselves. You build a system that learns and records its own discoveries directly into your dashboard.

Setup guide

Set up FullStory 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 FullStory 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="FullStory_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about FullStory MCP in AutoGen

Agents pass the JSON output from tools like `list_sessions` through their conversation history. They discuss the data points until they reach a conclusion.

Yes. You configure the agents to challenge each other's analysis of session events. This forces a deeper look at the raw data before a decision is made.

The server uses a zero-trust architecture. You define which tools the agents can call, ensuring they only access the data you authorize.

Every call to `list_users` or other tools is recorded in the agent conversation log. You review the transcript to see exactly what evidence the agents used.

You implement a policy where the agents must call `delete_user` if they process sensitive data that shouldn't persist. The agents follow your instructions to maintain compliance.

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