ContextQA MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ContextQA as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to ContextQA. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ContextQA?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine ContextQA tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the ContextQA MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from ContextQA
Why Use LlamaIndex with the ContextQA MCP Server
LlamaIndex provides unique advantages when paired with ContextQA through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ContextQA tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ContextQA tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ContextQA, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ContextQA tools were called, what data was returned, and how it influenced the final answer
ContextQA + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ContextQA MCP Server delivers measurable value.
Hybrid search: combine ContextQA real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ContextQA to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ContextQA for fresh data
Analytical workflows: chain ContextQA queries with LlamaIndex's data connectors to build multi-source analytical reports
ContextQA MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ContextQA to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting ContextQA to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpContextQA + LlamaIndex FAQ
Common questions about integrating ContextQA MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ContextQA with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ContextQA to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.