How to Use the ContextQA MCP in LangChain

Q: How do I pass ContextQA execution states to a LangChain agent?

You use the getexecution tool within your agent's toolset. The agent calls this tool to retrieve specific AI-healing run states, then uses that payload to decide the next step in its chain.

Q: Can a LangChain chain trigger a ContextQA run automatically?

Yes. Your chain can invoke the triggerrun tool to kick off a testing job. The tool returns run details that your chain can immediately pass to subsequent analysis steps.

Q: How does LangChain handle ContextQA environment mapping?

Your agent calls listenvironments to fetch the target layers. LangChain parses this list, allowing the agent to match test suites to the correct environment before triggering a run.

Q: Can I filter which test cases my agent sees?

Yes, you can use listcases to discover the routing limits of your test tree. This tool lets your agent inspect the structure of your cases so it only triggers the specific tests you need.

Run ContextQA tests and track AI-healing runs directly inside your LangChain reasoning chains.

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Connect ContextQA MCP to LangChain

Create your Vinkius account to connect ContextQA to LangChain 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 ContextQA with LangChain

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Chain ContextQA runs with LangChain agents

The ContextQA MCP Server lets your agent trigger live test runs and verify executions within a single LangChain run. By calling `trigger_run`, your agent starts a pipeline job and passes the output to the next step of your chain. You can feed these real-time test results directly into LangSmith to trace performance. Your agent uses `get_execution` to check the healing state of failed runs, letting you build self-correcting deployment pipelines that don't need manual oversight.

Audit API tests using LangChain chains

This MCP Server exposes your raw OpenAPI configurations directly to your LangChain schema. When your agent invokes `list_api_tests`, it pulls active REST specifications and parses them to find drift between your code and your tests. Your chains can compare these specs against current project boundaries mapped by `get_project`. This keeps your testing suites aligned with your actual code deployments without writing custom parsing scripts.

Map test environments with LangChain

Pulling environment target layers is simple when you combine this MCP Server with LangChain's structured output. Your agent runs `list_environments` to find active target layers and maps them directly to your deployment configurations. Once it identifies the environment, the agent pulls matching test suites using `list_suites`. This lets your chain dynamically decide which test suite to run depending on which staging server is currently active.

Setup guide

Set up ContextQA MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes ContextQA tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "contextqa-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent ContextQA transactions"
    })
    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 LangChain

You use the `get_execution` tool within your agent's toolset. The agent calls this tool to retrieve specific AI-healing run states, then uses that payload to decide the next step in its chain.

Yes. Your chain can invoke the `trigger_run` tool to kick off a testing job. The tool returns run details that your chain can immediately pass to subsequent analysis steps.

Your agent calls `list_environments` to fetch the target layers. LangChain parses this list, allowing the agent to match test suites to the correct environment before triggering a run.

Yes, you can use `list_cases` to discover the routing limits of your test tree. This tool lets your agent inspect the structure of your cases so it only triggers the specific tests you need.

Your API test configurations and execution run data never leave your local environment during tool execution. The Vinkius MCP Server runs inside a secure V8 sandbox, passing only the final tool outputs to your LangChain agent over an encrypted, single-token endpoint.

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