How to Use the ContextQA MCP in AutoGen

Q: How do AutoGen agents collaborate on ContextQA runs?

One agent can trigger a run using triggerrun, while another agent calls getexecution to track its progress. They exchange messages to decide if the run succeeded or if they need to debug.

Q: Can AutoGen agents verify ContextQA test environments?

Yes. Your agents call listenvironments to fetch active target layers. They discuss the configuration to confirm the staging environment is ready before starting any test suites.

Q: How do AutoGen agents handle ContextQA test cases?

Agents run listcases to discover the routing limits of your test tree. They analyze the structure together to divide up testing tasks and ensure complete coverage.

Q: How do I get project UUIDs using this server?

Your agents can invoke listprojects to identify bounded test environments. This tool returns the project mappings and UUIDs needed to route test executions correctly.

Let your AutoGen agents debate and trigger ContextQA tests to validate code quality.

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Setup ContextQA with AutoGen

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Debate ContextQA test runs in AutoGen

This MCP Server gives your AutoGen agents the tools they need to run tests and analyze results. One agent can call `trigger_run` to start a pipeline job, while a validation agent monitors the execution. The monitoring agent uses `get_execution` to check the healing state of the run. If a test fails, the agents debate whether the failure is a genuine code bug or an environment configuration issue.

Audit ContextQA API tests via AutoGen

Resolving API drift is easier when multiple agents use this MCP Server. Your developer agent uses `list_api_tests` to pull OpenAPI configurations, while a security agent checks them for compliance. They compare these specs against project boundaries retrieved by `get_project`. If they find discrepancies, the agents negotiate the necessary fixes before committing changes to your repository.

Manage ContextQA environments in AutoGen

Managing complex testing environments requires this MCP Server. Your manager agent calls `list_environments` to find active target layers and shares this configuration with your testing agents. The testing agents then use `list_suites` to pull matching GUI test suites for that environment. They coordinate which suites to run, ensuring you don't waste compute resources on redundant test runs.

Setup guide

Set up ContextQA 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 ContextQA 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="ContextQA_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ContextQA. 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 ContextQA MCP in AutoGen

One agent can trigger a run using `trigger_run`, while another agent calls `get_execution` to track its progress. They exchange messages to decide if the run succeeded or if they need to debug.

Yes. Your agents call `list_environments` to fetch active target layers. They discuss the configuration to confirm the staging environment is ready before starting any test suites.

Agents run `list_cases` to discover the routing limits of your test tree. They analyze the structure together to divide up testing tasks and ensure complete coverage.

Your agents can invoke `list_projects` to identify bounded test environments. This tool returns the project mappings and UUIDs needed to route test executions correctly.

Absolutely. Your test suites, API configurations, and run results are never stored or exposed. The MCP Server routes all tool calls through a zero-trust, ephemeral sandbox requiring only a single endpoint token.

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