ℹ️ Official Qase MCP Server repository and installation instructions are available here: https://github.com/qase-tms/qase-mcp-server
Qase MCP Server allows AI assistants such as Claude, Cursor, Claude Code, Codex CLI, and other MCP-compatible tools to interact directly with your Qase workspace.
Using natural language prompts, AI tools can create and manage test cases, runs, defects, suites, milestones, environments, and more through Qase APIs.
Using MCP (Model Context Protocol), AI tools can securely connect to Qase and help you work with:
Test cases
Test runs
Test results
Test plans
Defects
Suites
Milestones
Environments
Shared steps
Attachments
QQL searches
This makes it possible to manage and query Qase data using natural language prompts.
What can Qase MCP do?
Once configured, your AI assistant can help automate and simplify many QA workflows. Examples include:
Test case management
You can ask your AI assistant to:
Create new test cases
Update existing test cases
Organize suites
Bulk-create cases
Link external issues
Example prompt:
Create a login test case in project DEMO with steps for entering username and password.
Test run management
You can:
Create test runs
Execute runs
Add results to existing test runs
Complete runs
Generate public links
Example prompt:
Create a regression run for the Authentication suite and add passed results for cases.
QQL searches
Qase MCP supports QQL (Qase Query Language), allowing advanced searches directly through AI assistants.
Example prompts:
Find all failed test results from the last 7 days.
Show all open blocker defects in project DEMO.
Defect and issue management
You can:
Create defects
Update statuses
Resolve defects
Link Jira or external issues
Example prompts:
Resolve the defect - "Failed reset password"
And more... The server currently includes more than 80 available tools across Qase entities.
Supported AI clients
Qase MCP currently supports MCP-compatible clients such as:
Claude Desktop
Claude Code
Cursor
OpenAI Codex CLI
OpenCode
Other MCP-compatible applications
Installation
The recommended installation method is via npm:
npm install -g @qase/mcp-server
After installation, configure your preferred MCP-compatible AI client using the instructions provided in the GitHub repository.
📘 Full installation and configuration instructions are available here: https://github.com/qase-tms/qase-mcp-server