Qase 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 Qase as an MCP tool provider through 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 Qase. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Qase?"
)
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 Qase MCP Server
Connect your Qase workspace to any AI agent and integrate test management deeply into your development workflow.
LlamaIndex agents combine Qase tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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 Overviews — Retrieve all active projects, view health metrics, and get total counts of test cases, runs, and defects instantly
- Test Cases & Suites — Explore your test hierarchy, pull specific test steps, and check case automation statuses without opening the Qase dashboard
- Test Runs & Execution — List all test runs, monitor execution status (passed, failed, blocked), and dive deep into test run analytics
- Defects & Milestones — Track project milestones and extract all logged defects linked to failed test cases, complete with severity levels and issue links
The Qase 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 Qase to LlamaIndex via MCP
Follow these steps to integrate the Qase 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 Qase
Why Use LlamaIndex with the Qase MCP Server
LlamaIndex provides unique advantages when paired with Qase through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Qase tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Qase tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Qase, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Qase tools were called, what data was returned, and how it influenced the final answer
Qase + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Qase MCP Server delivers measurable value.
Hybrid search: combine Qase real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Qase 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 Qase for fresh data
Analytical workflows: chain Qase queries with LlamaIndex's data connectors to build multi-source analytical reports
Qase MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Qase to LlamaIndex via MCP:
get_case
Retrieves details for a specific test case
get_project
Retrieves details for a specific project
get_run
Retrieves details for a specific test run
list_cases
Lists test cases in a project
list_defects
Lists all defects linked to test case failures
list_milestones
Lists all milestones in a project
list_plans
Lists all test plans in a project
list_projects
Lists all projects in Qase
list_runs
Lists all test runs in a project
list_suites
Lists test suites in a project
Example Prompts for Qase in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Qase immediately.
"List all Qase projects and show me their overall health."
"Fetch the details of test case ID 45 in the WEB project."
"Are there any recent defects added for the WEB project?"
Troubleshooting Qase MCP Server with LlamaIndex
Common issues when connecting Qase to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpQase + LlamaIndex FAQ
Common questions about integrating Qase 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 Qase 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 Qase to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.