@checked/mcp
Related Servers
Alternatives to @checked/mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceProvides AI assistants with seamless access to SmartBear's suite of testing and monitoring tools, enabling querying testing data, analyzing performance metrics, and managing test automation through natural language.12,469 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to run, debug, and inspect Playwright end-to-end tests, returning structured pass/fail stats, failure diagnostics with actionable fix hints, live DOM inspection, selector validation, visual diffs, and flakiness verdicts instead of raw test output.998 npmMIT
- AlicenseAqualityBmaintenanceLets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.536 PyPIApache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to answer QA questions in a single call, covering run reports, failure grouping by root cause, flaky versus regressed test detection, and coverage gaps. Also exposes raw TestRail API access when a question needs it.MIT
- FlicenseAqualityDmaintenanceEnables AI agents to manage Autotest AI projects, screens, test cases, and test runs through natural language commands.10-

SmartBear MCP serverofficial
AlicenseBqualityAmaintenanceEnables AI assistants to interact with SmartBear's suite of testing and monitoring tools including BugSnag, Swagger, Reflect, and more through natural language, allowing querying of testing data, analysis of performance metrics, and management of test automation.30912,469 npm44MIT
TDQS
Scored across 19 tools
Most tools have clearly distinct resources/actions (projects, environments, runs, findings, issues, imports, scans, usage). A few could be confused—start_run vs start_scan on website projects, and list_issues/list_findings/get_issue/get_finding around problem retrieval—but the detailed descriptions explain the differences.
All tool names are snake_case and mostly follow a verb_noun pattern (list_projects, get_run, start_run, cancel_run). Minor deviations such as whoami, wait_for_run, and rerun_failed keep it from perfect consistency.
19 tools is in the heavy 16-25 range for a single integration. While most map to distinct entities, several granular run/finding retrieval tools (get_run_results, get_run_report, list_findings, get_finding) could be consolidated.
Core operational workflows are covered: inspect projects/environments/runs/findings/issues, import specs, start/rerun/cancel runs, scan websites, and check usage. However there are notable gaps in setup/admin—no create/update/delete for projects or environments, and issue/finding tools are read-only—so onboarding a new API project or editing configuration is a dead end.