Schema | AI QA Agent MCP

AI QA Agent MCP

Describes the environment variables required to run the server.

Name	Required	Description	Default
No arguments

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Features and capabilities supported by this server

Protocol revision2025-11-25

Functions exposed to the LLM to take actions

Name	Description
list_test_runsA	Find available Playwright JSON and JUnit test-result files in the approved reports directory.
get_test_run_summaryA	Read an approved Playwright JSON report and calculate pass, failure, skip, timeout, and flaky-test metrics.
analyze_test_failuresA	Analyze failed, timed-out, and flaky tests from an approved Playwright JSON report and group them by probable failure category.
generate_bug_reportA	Generate a structured draft bug report for one failed, timed-out, or flaky test from an approved Playwright JSON report.
generate_qa_summaryB	Generate a complete QA execution summary containing metrics, failure analysis, quality risks, Markdown output, and an advisory release recommendation.

Interactive templates invoked by user choice

Name	Description
`prepare_release_quality_report`	Guide an AI client through analyzing test evidence and preparing an advisory release-quality report.

Contextual data attached and managed by the client

Name	Description
`latest_test_run`	Read the newest supported Playwright JSON test run from the approved reports directory as normalized QA execution data.

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/sadi-qa/ai-qa-agent-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server