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 CrewAI 10 tools — connect in under 2 minutes

Name: ContextQA
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to ContextQA through the Vinkius — pass the Edge URL in the `mcps` parameter and every ContextQA tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="ContextQA Specialist",
    goal="Help users interact with ContextQA effectively",
    backstory=(
        "You are an expert at leveraging ContextQA tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in ContextQA "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* 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.

When paired with CrewAI, ContextQA becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call ContextQA tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from ContextQA

Why Use CrewAI with the ContextQA MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with ContextQA through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

ContextQA + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries ContextQA for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries ContextQA, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain ContextQA tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries ContextQA against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

ContextQA MCP Tools for CrewAI (10)

These 10 tools become available when you connect ContextQA to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

ContextQA + CrewAI FAQ

Common questions about integrating ContextQA MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.