squally-mcp
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- AlicenseNot gradedqualityCmaintenanceEnables AI agents to access observability and evaluation data, including run history, span traces, LLM-as-judge evaluation results, and regression reports.MIT

Tesults MCPofficial
AlicenseNot gradedqualityDmaintenanceConnect AI agents to your test results, insights, and targets. Query test runs, failures, flaky tests, and regressions across frameworks including Playwright, Jest, Pytest, Cypress and more.26 npmMIT- AlicenseAqualityAmaintenanceProvides coding agents with visibility into test health through tools for flaky test detection, test quality linting, and LLM evaluation harness, enabling them to triage failures, review test quality, and check prompt changes for regressions.9Apache 2.0
- AlicenseAqualityAmaintenanceProvides coding agents read-only access to Depot's CI failure diagnoses, container build forensics, run history, cache effectiveness, registry contents, and usage data through MCP tools.2839 npm1-
- FlicenseAqualityBmaintenanceAllows LLM agents to query Playwright JSON test results for run summaries and failure information.2-
- AlicenseAqualityAmaintenanceConnects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.353 npm1MIT
TDQS
Scored across 7 tools
Each tool targets a distinct resource and action: project discovery, run search, run detail, failure debugging, single-test status, flaky-test listing, and error-signature aggregation. The descriptions explicitly cross-reference when one tool should be preferred over another, so there is no real ambiguity.
All tools share a consistent 'squally-' prefix and lowercase hyphenated action-object pattern such as list-projects, find-run, get-run, and debug-failure. The verbs are semantically meaningful and consistently used throughout.
Seven tools is a well-scoped size for a CI/test debugging server. Each tool covers a distinct step in the investigation workflow without redundant or bloated additions.
The surface covers the full read-only debugging journey: find a project, locate runs, inspect individual run results, drill into a failing test's attempts, check single-test flakiness, list flaky tests, and aggregate error signatures. There are no obvious dead ends or missing operations for the stated purpose.