How to Use the PractiTest MCP in AutoGen

Q: How do I configure PractiTest tools for AutoGen?

Import mcpservertools and pass your endpoint via StreamableHttpServerParams. AutoGen's McpToolAdapter automatically converts the JSON schemas for your agents.

Q: How does AutoGen handle PractiTest API errors?

If a createtest call fails due to malformed JSON, the server returns the error to the chat. The execution agent reads the failure, corrects the syntax, and retries automatically.

Q: Can I require human approval before an agent logs a test run?

Yes. Configure your user proxy agent to intercept calls to createrun. The system will pause and ask for your confirmation before writing anything to the database.

Give your AutoGen agents the ability to debate, draft, and execute PractiTest scenarios via MCP.

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Setup PractiTest with AutoGen

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Multi-agent requirement analysis

Complex QA demands multiple perspectives. You can assign one AutoGen agent to act as a product owner, using `list_requirements` and `get_requirement` to pull the latest specs into the group chat. A second agent acts as the lead tester. It reviews those requirements and checks existing coverage via `list_tests`, sparking a debate over whether new scenarios are actually necessary.

Consensus-driven test execution via the MCP Server

Agents don't just talk; they act. Once the group agrees on a testing strategy, the execution agent formats a JSON payload and calls `create_instance` to prep the environment. Following the actual test execution, another agent logs the outcome using `create_run`. The system requires agreement before writing data, preventing rogue scripts from polluting your QA metrics.

Autonomous project auditing in AutoGen

Keeping test repositories clean is tedious manual work. A dedicated auditing agent can periodically run `list_projects` and `list_instances` to map out your entire workspace structure. If it finds orphaned tests or undocumented runs, it alerts the human overseer. You get a self-maintaining QA ecosystem powered by continuous, conversational analysis.

Setup guide

Set up PractiTest 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 PractiTest 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="PractiTest_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about PractiTest MCP in AutoGen

Import mcp_server_tools and pass your endpoint via StreamableHttpServerParams. AutoGen's McpToolAdapter automatically converts the JSON schemas for your agents.

Yes. You pass the generated tool list to any AssistantAgent in the chat. They will all route requests through the same underlying client.

If a `create_test` call fails due to malformed JSON, the server returns the error to the chat. The execution agent reads the failure, corrects the syntax, and retries automatically.

Yes. Configure your user proxy agent to intercept calls to `create_run`. The system will pause and ask for your confirmation before writing anything to the database.

The MCP server processes requirement text, test steps, and project metadata. Vinkius handles the transport over an ephemeral, zero-trust connection, ensuring chat logs never expose your core IP.

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