Qase MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Qase through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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({
"qase": {
"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 Qase, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Qase through native MCP adapters. Connect 10 tools via 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 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 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 Qase to LangChain via MCP
Follow these steps to integrate the Qase 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 Qase via MCP
Why Use LangChain with the Qase MCP Server
LangChain provides unique advantages when paired with Qase through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Qase 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 Qase queries for multi-turn workflows
Qase + LangChain Use Cases
Practical scenarios where LangChain combined with the Qase MCP Server delivers measurable value.
RAG with live data: combine Qase tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Qase, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Qase tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Qase tool call, measure latency, and optimize your agent's performance
Qase MCP Tools for LangChain (10)
These 10 tools become available when you connect Qase to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Qase to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersQase + LangChain FAQ
Common questions about integrating Qase MCP Server with LangChain.
How does LangChain connect to MCP servers?
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?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.