Can my agent help me identify the root cause of a failed test case?

Yes. Use the 'get_case' tool to resolve the AI root-cause model for a specific test. The agent retrieves the definitions and AI insights to confirm exactly why a boundary was breached during execution.

How do I trigger a full test suite run via chat?

Provide the 'project_id' and 'suite_id' to your agent and use the 'trigger_run' mutation. The agent will command the backend to queue the tests against live ContextQA test clusters instantly.

Can I see screen captures of failed test steps through the agent?

The 'get_execution' tool retrieves detailed logs tracking failing step boundaries. Where supported by ContextQA, the agent can surface the associated physical screen capture limits to help you visualize the failure.

ContextQA MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: ContextQA
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect ContextQA through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "contextqa": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using ContextQA, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ContextQA MCP Server

Connect your ContextQA account to any AI agent and take full control of your context-aware AI testing platform through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with ContextQA through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Project & Suite Management — List bounded test environments and perform structural extraction of GUI test suites across your projects
AI-Healing Executions — Monitor active test runs and inspect specific AI-healing states, including failing step boundaries and screen captures
Automated Triggers — Dispatch live testing commands to queue suites against ContextQA test clusters directly from your workspace
API & Swagger Testing — Enumerate automated HTTP assertions and explicitly verify structural payloads against OpenAPI configurations
Environment Auditing — List physical runtime URLs and group active contexts to verify testing boundaries across different layers
Test Case Inspection — Resolve AI root-cause models and validate specific case definitions to identify precise points of failure

The ContextQA MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect ContextQA to LangChain via MCP

Follow these steps to integrate the ContextQA MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from ContextQA via MCP

Why Use LangChain with the ContextQA MCP Server

LangChain provides unique advantages when paired with ContextQA through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine ContextQA MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across ContextQA queries for multi-turn workflows

ContextQA + LangChain Use Cases

Practical scenarios where LangChain combined with the ContextQA MCP Server delivers measurable value.

RAG with live data: combine ContextQA tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query ContextQA, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain ContextQA tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every ContextQA tool call, measure latency, and optimize your agent's performance

ContextQA MCP Tools for LangChain (10)

These 10 tools become available when you connect ContextQA to LangChain via MCP:

get_case

Validate Data Science object extraction tracking explicit steps boundaries

get_execution

Execute static queries targeting exactly specific AI-healing Run states

get_project

Retrieve explicit Project mapping UUIDs analyzing execution spaces limitlessly

list_api_tests

Extracts native REST & OpenAPI testing configurations natively

list_cases

Discover explicit routing limits structuring ContextQA cases trees

list_environments

List static configurations mapping Environment target layers mapping limits

list_executions

Inspect deep internal interaction tracking explicit global Run chunks

list_projects

Identify bounded ContextQA test environments grouping automated validations

list_suites

Perform structural extraction matching asynchronous GUI test Suites payloads

trigger_run

Dispatch a live testing command routing explicit Jobs against pipelines

Example Prompts for ContextQA in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with ContextQA immediately.

"List all test suites for project 'vinkius-app-prod'"

"Trigger a run for suite 'Checkout-Flow' in project 'vinkius-app-prod'"

"Show me why the last execution of project 'mobile-app' failed"

Troubleshooting ContextQA MCP Server with LangChain

Common issues when connecting ContextQA to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

ContextQA + LangChain FAQ

Common questions about integrating ContextQA MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.