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    • A
      license
      A
      quality
      C
      maintenance
      Gives AI assistants structured access to a test automation stack so they can run suites, inspect failure details and captured output, look up test data, and file Jira bugs (or drafts) only for confirmed defects. It works with any framework that emits JUnit XML, letting assistants triage flaky, environment, and test-data issues alongside real product defects.
      6
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Provides 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.
      15,685 npm
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to run configured test suites, inspect failures and logs, compare runs, and draft or file bug reports only after human confirmation, with safeguards such as selector validation, environment isolation, timeouts, and redaction.
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Connects MCP clients such as Claude Code, Cursor and VS Code to a hosted QA service so the assistant can start and monitor test runs on a web app, explore its pages and flows, and read the bugs those runs uncover. Access is scoped by API key permissions, letting read-only keys list projects and issues while write-enabled keys trigger or stop runs.
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables 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.
      11
      2,464 npm
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Lets 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.
      5
      19 PyPI
      Apache 2.0

    TDQS

    B3.4/5.0

    Scored across 19 tools

    Disambiguation4/5

    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.

    Naming Consistency4/5

    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.

    Tool Count3/5

    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.

    Completeness3/5

    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.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues