How to Use the Applitools MCP in AutoGen

Q: How do I set up Applitools with AutoGen?

Install the autogen-extmcp package. Initialize your tools using mcpservertools with a StreamableHttpServerParams URL, then pass them to your AssistantAgent.

Q: How do agents handle batch statistics?

One agent calls getbatchstats to get a quick summary of passed and failed tests. If the failure rate is high, it alerts other agents to investigate further.

Deploy multi-agent systems that debate visual test results and manage Applitools baselines via AutoGen.

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Create your Vinkius account to connect Applitools 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 Applitools with AutoGen

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Debate Applitools MCP Server results

AutoGen agents do not just blindly accept test failures. A QA agent pulls unresolved tests using `get_batch_stats`, while a developer agent argues that the UI changes are intentional. They negotiate the outcome before making a final decision. This consensus-driven approach prevents accidental deletions. If one agent suggests calling `delete_baseline` because a page was redesigned, a security agent challenges that action. They review the branch data together before executing the tool.

Cross-reference test batches

Multiple agents tackle different parts of your visual testing pipeline simultaneously. One agent runs `list_batches` to identify failed runs, while another immediately calls `get_session` to extract the technical details. They share findings in a shared conversation thread. You build systems where complex UI regressions require deliberation. The agents pull all available test results via `list_results` and discuss the impact. They converge on a summary report that explains exactly what broke and why.

Manage branch environments

Feature branches often introduce visual drift that needs careful review. Your agents monitor these changes by calling `list_branch_baselines` to inspect the visual state of a specific branch. They compare the new baseline against the main branch configuration. The system verifies everything is working before running tests. An agent calls `validate_key` to check API connectivity, then coordinates with the rest of the swarm to begin the analysis. You get a fully autonomous QA team reviewing your UI.

Setup guide

Set up Applitools 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 Applitools 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="Applitools_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Applitools_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Install the autogen-ext[mcp] package. Initialize your tools using mcp_server_tools with a StreamableHttpServerParams URL, then pass them to your AssistantAgent.

Yes. A typical setup involves a tester agent fetching data via `get_batch` and a reviewer agent analyzing those results. They discuss the findings in a shared chat.

You should implement human-in-the-loop controls. While agents negotiate the need to call `delete_baseline`, requiring a human to approve irreversible actions is always a smart move.

One agent calls `get_batch_stats` to get a quick summary of passed and failed tests. If the failure rate is high, it alerts other agents to investigate further.

Your UI diffs and test session logs are never exposed to external listeners. Vinkius secures the MCP connection through an isolated, ephemeral runtime that destroys all execution context the moment the agent conversation ends.

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