K-Guard MCP
K-Guard MCP
Старший коллега по очкам · Старший коллега по вайбкодингу
Поток создания не прерываем, но выпуск не пропускаем кое-как.
K-Guard MCP — это локально-ориентированный MCP-аудитор, который перед выпуском результатов вайбкодинга проверяет вместе с вами сайт, экспозицию API, управление данными и операционные риски. Guardian различает «проблем нет» и «ещё не видел», возвращая вердикт можно выпускать / исправить и выпускать / недостаточно объёма проверки и следующие действия на корейском языке.
K-Guard не является гарантийным документом, заменяющим человеческие бизнес-решения или юридическую проверку. Он автоматизирует вопросы, которые старший разработчик задаёт перед выпуском, и fail-closed шлюзы.
Установка за 5 минут · Установка и проверка дизайна · Руководство по использованию · Инструкция по публикации на GitHub · Обязательная квалификация · Северная звезда продукта · Полная оценка dogfood · Подготовка к конкурсу 2026
Первые 5 минут: текущий исходный код
Пока не предполагается публикация в открытых индексах пакетов. При первом запуске установите текущий исходный код из корня репозитория и следуйте следующим действиям, которые показывает старший коллега по очкам.
python -m pip install .
k-guard install --client auto --profile local-dev --workspace .
k-guard doctor --client autoauto находит установленные Grok, Codex, Antigravity, объединяет MCP k-guard в пользовательскую область, а если есть OpenAI tunnel-client, подготавливает локальный профиль для ChatGPT. --workspace . фиксирует текущий проект как private binding. После этого, даже если MCP запускается из другого рабочего каталога, он проверяет этот проект, а пути за пределами границы отклоняет как high HOLD. Для ChatGPT doctor/run также требуется CONTROL_PLANE_API_KEY с правами Tunnels Read + Use в текущей оболочке. Поскольку после этого для ChatGPT остаются tunnel-client run и подключение приложения в режиме разработчика, установщик не приукрашивает успех и возвращает частичное подключение · требуются следующие шаги. Существующие настройки сохраняются, перед изменением JSON создаётся резервная копия, а ключи доказательств оператора и исходный текст workspace сохраняются только в пользовательских файлах ~/.k-guard. local-dev включает только localhost read-only probe и небольшой фиксированный углублённый путь, внешние и сессионные probe остаются выключенными. Если нужна только статическая проверка, используйте --profile workspace.
Базовый вывод установки и диагностики состоит из трёх этапов: готовность подключения → AI-клиент → следующие действия. Для автоматизации используйте install --json, doctor --json. Для воспроизводимости судейства конкурса и релиза, а также для исправления кода используйте путь проверки судейства и воспроизводимости из установки за 5 минут, устанавливая build и evidence lock с --require-hashes. Semgrep 1.174.0 и pip-audit 2.10.1 также устанавливаются с полным hash closure из requirements-semgrep.lock и requirements-pip-audit.lock соответственно в изолированный venv, не изменяя проверяемую среду выполнения.
В MCP-клиенте:
안경선배로 이 앱을 끝까지 봐줘. check_my_app으로 전체 검수를 시작하고,
continue_review를 state=completed 또는 failed가 될 때까지 반복해줘.
완료 전에는 통과라고 말하지 마. 코드를 고쳤다면 check_my_app을 다시 완료해줘.
출하 전에는 실제 사업 목적과 범위를 확인한 뒤 최신 review_id로
start_review_before_ship을 실행하고 다시 continue_review로 Guardian 완료까지 기다려줘.
experience.presentation 순서대로 판정, 이유, 다음 행동 3개만 먼저 말해줘.check_my_app — это не проверка, которая кое-как завершается за 5 секунд, а инструмент приёма полной аудиторской проверки всего workspace. Первый ответ — review_in_progress, и до завершения continue_review вердикт о выпуске не выносится. Проверяются также текстовые артефакты для развёртывания в dist, build, .next; если количество кандидатов, SHA-256 путей и завершение чтения не совпадают, блокируется как high. По завершении выдаётся review_receipt, привязанный к SHA-256 workspace и дерева исходников. Если код изменился, начать проверку выпуска со старым review_id нельзя — нужно заново завершить check_my_app.
Related MCP server: VibeGuard MCP Server
Вердикт старшего коллеги по очкам
Код | Значение | Следующие действия |
| Можно выпускать в заданном объёме и по high-критериям | Сохранить отчёт и доказательства того же коммита |
| Есть blocking finding | После |
| Недостаточно доказательств динамической проверки, потоков, целей, объёма | Сначала заполнить пропущенные области проверки |
| Недостаточно доказательств привязки к одному приложению и подписи оператора | Повторно запустить с тем же |
| Независимо от риска приложения недостаточно доказательств квалификации deep·SCA·runtime·DB·field инструмента аудита | Проверить раздельно |
| Создать только отчёт для рассмотрения | Запустить вердикт о выпуске с |
Все основные результаты предоставляют совместимый с experience.presentation алиас experience.details.presentation, порядок фиксирован: verdict → why → next_actions → details. Guardian показывает риск приложения отдельно как application_assurance, а статус валидации самого инструмента аудита — как auditor_qualification. experience.verdict_code, experience.verdict_message, experience.summary — это поля, совместимые с существующими отчётами.
Что проверяется
K-Guard объединяет:
Статическое сканирование: корейские PII, составные PII, корейские идентификаторы организаций (синтаксическая проверка контрольной суммы 사업자등록번호; распознавание 법인등록번호 по историческому приложению контрольной суммы или по явному контексту/синтаксису 4+2+7 после 2025-01-31, без проверки в живом реестре), секреты
Сканирование конфигурации: риски CORS/debug/source map/env prefix
Сканирование рисков уровня старшего разработчика: прямые request-to-SQL/command/file/URL/HTML sink'и, массовое присваивание, open redirect, хранение токенов в браузере, слабые cookie, сравнение паролей в открытом виде, JWT decode без verify
Сканирование корейского управления данными: объявления уникальных/чувствительных полей, доказательства шифрования и контроля доступа, журналирование доступа, проверка внешних обработчиков, сроки хранения и удаление
MCP-нативное сканирование угроз: скрытые инструкции, отравление инструментов, намерение эксфильтрации и чрезмерно широкий локальный доступ по прямому тексту плюс ограниченные Unicode/confusable, escaped/percent/HTML, Base64, split-fragment и трёхстрочная скользящая нормализация
Структурированная проверка MCP-конфигурации: shell-обёртки, изменяемые загрузчики пакетов, обычный удалённый HTTP, фиксированные учётные данные, небезопасные ссылки/ошибки чтения и fail-closed границы обнаружения по кандидатам конфигурации project/Codex/Grok/Antigravity
Локальный набор правил YARA-lite: маркеры prompt-injection, маркеры команд эксфильтрации, намёки на массовые корейские схемы
Безопасный динамический probe: проверки localhost HTTP, явно авторизованные проверки внешних источников, опциональные ограниченные проверки глубокой экспозиции, а также опциональные предоставленные пользователем read-only заголовки сессий
Карта рисков потоков данных: эвристический граф плюс Python AST и облегчённый JS/TS анализ taint source -> sink, включая сводки параметр-в-sink для уникальных импортов Python-хелперов и ограниченные сводки IDOR route-to-service для JS/TS
Наблюдатель/перехватчик MCP runtime: проверки событий JSON/JSONL и stream-ready на скрытые инструкции, потоки PII в агентные/внешние sink'и, а также пакетное блокирование/редактирование для пересылаемых потоков событий
Глубокий адаптер Semgrep: офлайн-профиль из 40 правил, точное доказательство сканируемой цели, фиксированная идентичность
1.174.0, привязка к снимку исходников и fail-closed находки для JS/TS/Python/Java/Kotlin/Go/PHP/Ruby/C#Пакет валидации на девяти языках: Python/JavaScript/TypeScript/Java/Kotlin/Go/PHP/Ruby/C# 90 закреплённых уязвимых/чистых случаев, оценка TP/FN/FP/TN и точное воспроизведение отпечатка за два запуска
Анализ состава программного обеспечения: ограниченное покрытие манифестов и lock-файлов Python/npm/Go с
pip-audit,npm auditиgovulncheck; частичный сбой движка, lock, вывода или дрейфа исходников блокирует выпускStreamable HTTP MCP proxy: полное посредничество POST/GET SSE/DELETE, жизненный цикл, привязанный к субъекту, default-deny JIT/JEA гранты, фильтрованный инвентарь инструментов, ограниченные потоки и HMAC-цепочные журналы аудита без сырых данных
Контроль базы данных: SQLGlot AST allowlist, RBAC по роли/базе/схеме/таблице/колонке, EXPLAIN-перед-чтением, SQLite query-only авторизатор, ограничение путей и бюджеты строк/ячеек/результатов/времени
Read-only коннекторы: локальный SQLite, журналы, сэмплирование хранилища JSON/CSV/TSV без хранения сырых значений; пробелы коннекторов становятся high-находками
Проверка хранения/удаления: отмечает отсутствие маркеров хранения/удаления при наличии сигналов персональных данных
Межуровневые вердикты: коррелирует находки корейских PII с доказательствами sink'ов LLM/MCP/внешних/данных в покое
Табло FP/FN: полнота recall по корпусу фикстур и доля ложных срабатываний. FPR использует
measurable_negative_countкак знаменатель (чистые негативы безexpected_absent);negative_count— все объявленные негативы, аtargeted_absence_case_countпокрывает отсутствия по выбранным правилам, которые не входят в знаменатель FPR. Нулевой измеримый негативный знаменатель сообщается как FPR 0.0.Вывод SARIF/CI: сбой сборки при настроенных уровнях серьёзности
Профиль Guardian
korean_senior: fail-closed четырёхдоменный контракт для безопасности сайта, экспозиции API, управления данными и операционного рискаОсновные MCP-инструменты:
check_my_app,continue_review,start_review_before_ship; внутренний канонический движок: Guardian high; управляемый промпт:guided_reviewРасширенные MCP-инструменты:
scan_workspace,deep_analyzer_audit,validate_multilang_pack,software_composition_audit,validate_policy_controls,validate_streamable_http_runtime,scan_text,scan_diff,scan_mcp_config,probe_http,build_flow_map,observe_mcp_events,enforce_mcp_events,observe_mcp_event,score_fixture_corpus,data_release_gate,create_field_campaign_template,field_campaign_status,create_guardian_manifest_template,guardian_audit,explain_rule,suggest_fix,security_gate
Границы установки
Пользователь, получивший requirements-evidence.lock, распространяемый вместе с точным wheel, созданным release workflow, может воспроизвести проверенное closure зависимостей без репозитория, как показано ниже. k-guard, k-guard-dashboard, k-guard-mcp, python -m k_guard_mcp.cli, python -m k_guard_mcp.server — это команды установленного wheel.
Релиз также включает SHA256SUMS, SLSA provenance bundle, подписанный GitHub OIDC, и CycloneDX SBOM attestation bundle. В онлайне проверьте субъекта сборки и digest с помощью gh attestation verify ./k_guard_mcp-0.1.0-py3-none-any.whl -R windmillstudio/k-guard-mcp, затем устанавливайте. Если checksum совпадает, но attestation не проверен, артефакт не считается официальным релизным.
python -m pip install --require-hashes -r ./requirements-evidence.lock
python -m pip install --no-deps ./k_guard_mcp-0.1.0-py3-none-any.whl
k-guard --help
k-guard install --client auto --profile local-dev --workspace .
k-guard doctor --client autoНа управляемых ПК с Windows, где политика контроля приложений блокирует консольный загрузчик k-guard.exe от pip, используйте точку входа модуля того же wheel. Контракт функций и вердиктов идентичен.
python -m k_guard_mcp.cli --help
python -m k_guard_mcp.cli install --client auto --profile local-dev --workspace .
python -m k_guard_mcp.cli doctor --client autoУстановка только wheel предоставляет только команды времени выполнения. Релизный sdist и checkout репозитория включают scripts/..., tests/fixtures/... и шаблоны валидации. Среда разработки подготавливается из корня репозитория установкой двух lock-файлов с --require-hashes, затем python -m pip install --no-build-isolation --no-deps -e .. Evidence lock фиксирует вместе версии релизного closure зависимостей и допустимые SHA-256; лицензии, уязвимости, SBOM и fresh-wheel smoke используют тот же файл.
Комната проверки старшего коллеги по очкам
Чтобы просмотреть авторизованный сайт на экране, запустите локальную панель управления.
k-guard-dashboard --port 8765Откройте в браузере http://127.0.0.1:8765/. Экран идёт в порядке вердикт старшего → находки → журнал проверки → карта экспозиции, и окончательное право выпуска принадлежит не экрану, а только start_review_before_ship в MCP. Брендовые изображения — это оригинальные активы, созданные для проекта, их происхождение фиксируется в docs/brand-assets.md.
CLI установленного wheel
Среди команд ниже tests/fixtures/korean_fixture_corpus.json — это публичный пример корпуса, который можно запустить прямо из checkout исходников. Пользователь, установивший только wheel, заменяет его на путь собственного фикстура той же схемы.
python -m k_guard_mcp.cli scan .
python -m k_guard_mcp.cli scan . --json report.json --markdown report.md
python -m k_guard_mcp.cli scan . --sarif k-guard.sarif --fail-on high
python -m k_guard_mcp.cli probe http://localhost:3000
python -m k_guard_mcp.cli probe http://localhost:3000 --deep-active
python -m k_guard_mcp.cli probe http://localhost:3000 --session-file session.headers.json --json
python -m k_guard_mcp.cli probe https://staging.example.com --allow-external --authorization-note "owned staging domain" --json
python -m k_guard_mcp.cli flow . --svg flow.svg --html flow.html
python -m k_guard_mcp.cli observe-mcp --events mcp-events.jsonl --json
python -m k_guard_mcp.cli mcp-proxy --report mcp-proxy-report.json --response-timeout 30 -- python -m your_upstream_mcp_server
python -m k_guard_mcp.cli mcp-http-proxy --upstream http://127.0.0.1:9000/mcp --port 8765 --report mcp-http-proxy.json --receipt-log mcp-http-proxy.receipts.jsonl
python -m k_guard_mcp.cli deep-analyze . --output deep-analyzer.json
python -m k_guard_mcp.cli language-validate --output language-validation.json
python -m k_guard_mcp.cli sca . --output sca.json
python -m k_guard_mcp.cli control-validate --output control-validation.json
python -m k_guard_mcp.cli runtime-validate --output runtime-validation.json
python -m k_guard_mcp.cli access-policy-template --output access-policy.json
python -m k_guard_mcp.cli agent-grant --policy access-policy.json --output agent.jwt --app-id APP --session-id SESSION --purpose RELEASE_REVIEW --subject AGENT --role release-reviewer --method tools/list --method tools/call --tool check_my_app --tool start_review_before_ship
python -m k_guard_mcp.cli score-corpus --corpus tests/fixtures/korean_fixture_corpus.json --output fixture-metrics.json --json
python -m k_guard_mcp.cli benchmark-template --output benchmarks/field-benchmark-template.csv
python -m k_guard_mcp.cli benchmark --manifest benchmarks/field-benchmark-template.csv --output field-benchmark.json --markdown field-benchmark.md --html field-benchmark.html
python -m k_guard_mcp.cli guardian-template --output benchmarks/guardian-targets.csv
python -m k_guard_mcp.cli suppression-template --output .k-guard/suppressions.csv
python -m k_guard_mcp.cli field-campaign-template --output benchmarks/field-app-roster.csv
python -m k_guard_mcp.cli field-campaign-status --roster benchmarks/field-app-roster.csv --output field-campaign-status.json
python -m k_guard_mcp.cli field-validation-template --ground-truth-output benchmarks/field-ground-truth.csv --review-output benchmarks/field-review.csv
python -m k_guard_mcp.cli field-validation-sign --ground-truth benchmarks/field-ground-truth.csv
python -m k_guard_mcp.cli field-validation-preregister --roster benchmarks/field-app-roster.csv --ground-truth benchmarks/field-ground-truth.csv --custodian-id split-custodian-a --output benchmarks/field-preregistration.json
python -m k_guard_mcp.cli guardian --manifest benchmarks/guardian-targets.csv --output guardian-report.json --markdown guardian-report.md --html guardian-report.html
python -m k_guard_mcp.cli guardian --manifest benchmarks/guardian-targets.csv --suppressions .k-guard/suppressions.csv --fail-on high --run-probes --run-sca --language-validation-report language-validation.json --mcp-http-proxy-report runtime-validation.json --control-validation-report control-validation.json --field-validation-report field-validation.json --output guardian-gate.json
python -m k_guard_mcp.cli field-validation-queue --guardian-report guardian-gate.json --output benchmarks/field-review.csv
python -m k_guard_mcp.cli field-validation-sign --review benchmarks/field-review.csv
python -m k_guard_mcp.cli field-validation-report --guardian-report guardian-gate.json --repeat-guardian-report guardian-gate-repeat.json --roster benchmarks/field-app-roster.csv --ground-truth benchmarks/field-ground-truth.csv --review benchmarks/field-review.csv --preregistration benchmarks/field-preregistration.json --profile field --output field-validation.json
python -m k_guard_mcp.cli mcp-intercept --events mcp-events.jsonl --forwarded-output forwarded.jsonl --report intercept-report.json --app-id YOUR_APP_ID --session-id RELEASE_SESSION_ID --guardian-report guardian-gate.json --fail-on-block
python -m k_guard_mcp.cli data-release-gate --guardian-report guardian-gate.json --guardian-manifest .k-guard/guardian-targets.csv --validation-source-guardian-report validation-source-guardian.json --validation-repeat-guardian-report validation-source-guardian-repeat.json --validation-report field-validation.json --validation-review benchmarks/field-review.csv --validation-ground-truth benchmarks/field-ground-truth.csv --validation-preregistration benchmarks/field-preregistration.json --validation-roster benchmarks/field-app-roster.csv --korean-fixture-corpus tests/fixtures/korean_fixture_corpus.json --korean-corpus-report fixture-metrics.json --mcp-intercept-report intercept-report.json --mcp-forwarded-output forwarded.jsonl --output data-release-gate.jsonsession.headers.json недолговечен и привязан к одному точному источнику. Guardian считает аутентифицированное сравнение завершённым только тогда, когда ответ с заявленной личностью также совпадает:
{
"origin": "http://localhost:3000",
"expires_at": "2026-07-14T12:30:00+09:00",
"headers": {"Cookie": "session=<local-test-session>"},
"identity_assertion": {
"path": "/api/me",
"expected_status": 200,
"expected_body_sha256": "<64 lowercase hex characters>"
}
}Держите файл внутри вызываемого workspace или рядом с манифестом Guardian. Файлы за пределами этой границы, ссылки, истёкшие сессии, несовпадающие источники, неподдерживаемые заголовки и файлы слишком большого размера завершаются сбоем (fail closed). Digest покрывает точные ограниченные байты ответа до декодирования UTF-8; сырые значения сессий и тела ответов не записываются в находки.
Валидация только для checkout исходников
Приведённые ниже scripts/** и pytest — это не пользовательские команды, входящие в wheel.
python -m pytest -q
python scripts/site_scale_calibration.py
python scripts/deep_probe_synthetic_calibration.py --targets 500 --output tmp/deep-probe-synthetic-500.json --markdown tmp/deep-probe-synthetic-500.md --html tmp/deep-probe-synthetic-500.html
python scripts/inner_core_product_gate.py --targets 500 --output tmp/inner-core-product-gate-500.json --markdown tmp/inner-core-product-gate-500.md --html tmp/inner-core-product-gate-500.html
python scripts/mcp_evasion_calibration.py --output evidence/regression/mcp-evasion-normalization.json
python examples/contest-demo/v1/demo.py run
python scripts/contest_readiness.py --output contest-readiness-report.json --markdown contest-readiness-report.md
python scripts/build_top_domain_manifest.py --output datasets/tranco-passive-homepage-10k.csv --limit 10000
python scripts/passive_homepage_calibration.py --targets datasets/tranco-passive-homepage-10k.csv --output reports/passive-homepage-10k-summary.json --checkpoint-jsonl reports/passive-homepage-10k-checkpoint.jsonl --max-targets 10000 --delay-ms 500Порог Coverage 90.00% для полной регрессии исходников — это combined coverage с включением ветвей, вычисляемый вместе по возможностям statement и branch. Это не означает, что сами ветви покрыты на 90%; используются precision=2 и fail_under=90, поэтому 89.90% — это провал. Область public_source_only CI публичного репозитория исключает отдельные доказательства развёртывания и нативные случаи, поэтому применяются пороги 88% в целом и 85% для redaction.py; эти два числа не взаимозаменяемы.
Гигиена релиза из checkout исходников
Используйте проверку гигиены релиза перед рассмотрением CTO или подготовкой публикации:
python scripts/release_hygiene.py --jsonРежим по умолчанию проверяет, что грязное рабочее дерево релизного кандидата классифицировано, свободно от видимых временных артефактов выполнения и свободно от нерешённых артефактов, требующих рассмотрения. Используйте --strict-clean после предполагаемого релизного коммита; строгий режим также сравнивает каждый байт входных данных сборки пакета с его blob в Git index. Тегированная публикация дополнительно требует --expected-tag v{project.version}, и этот точный тег должен разрешаться в проверенный коммит HEAD. Release workflow ожидает переиспользуемую матрицу CI Windows/macOS/Linux, создаёт attestations и публикует тегированные артефакты как GitHub Release. Подробности в docs/release-hygiene.md.
Шаблон Guardian Actions в настоящее время устанавливает точный checkout исходников K-Guard, выбранный коммитом workflow; он не предполагает пакет PyPI. Перед копированием в другой репозиторий разместите K-Guard по пути, закреплённому за коммитом, и задайте K_GUARD_SOURCE_PATH. После появления релизных артефактов потребители должны закреплять либо точный wheel с опубликованным SHA-256, либо полный 40-символьный Git-коммит в требовании прямой ссылки. Заменяйте плейсхолдеры только на фактическое место релиза:
k-guard-mcp @ https://<artifact-host>/<exact-wheel>.whl#sha256=<published-sha256>
k-guard-mcp @ git+https://<repository-url>.git@<40-character-commit>Основная лицензия K-Guard — MIT. Атрибуция вышестоящего проекта Apache-2.0, проверенные ревизии, сопоставление изменённых файлов и границы исключённых лицензий задокументированы в THIRD_PARTY_NOTICES.md и docs/upstream-adoption.md.
MCP
python -m pip install --require-hashes -r requirements-build.lock
python -m pip install --require-hashes -r requirements-evidence.lock
python -m pip install --no-build-isolation --no-deps .
k-guard-mcpMCP probe_http отключён по умолчанию. Включайте его только для доверенных аудиторских сессий:
K_GUARD_MCP_ENABLE_PROBE=1 python -m k_guard_mcp.serverАутентифицированное чтение через MCP также требует K_GUARD_MCP_ENABLE_SESSION_PROBE=1 и кратковременного локального session_file, привязанного к источнику. Значения заголовков читаются локально и не записываются в находки. Сам по себе заголовок никогда не доказывает вход в систему: Guardian требует, чтобы необязательный identity_assertion совпал, прежде чем аутентифицированное сравнительное покрытие сможет пройти.
Ограниченное глубокое активное зондирование через MCP также требует K_GUARD_MCP_ENABLE_DEEP_ACTIVE_PROBE=1 и probe_http(..., deep_active=true). Этот режим проверяет небольшой фиксированный список открытых путей env/git/backup/debug и по-прежнему избегает попыток входа, мутаций, фаззинга и эксплойт-пейлоадов.
Авторизованное внешнее зондирование через MCP также требует K_GUARD_MCP_ENABLE_EXTERNAL_PROBE=1 плюс либо K_GUARD_MCP_EXTERNAL_ALLOWED_HOSTS=example.com,staging.example.com, либо external_authorized=true с authorization_note при каждом вызове. Отчёт фиксирует основание авторизации с помощью DYN_EXTERNAL_TARGET_AUTHORIZATION_AUDIT.
Установка и подключение настроены для четырёх клиентов: ChatGPT, Grok, Codex, Antigravity. Подробные процедуры приведены в docs/quickstart-ko.md и docs/mcp-client-install.md.
При подготовке отчёта о результатах конкурса и фактических доказательств совместимости используйте docs/contest-2026-result-report-draft-ko.md и docs/client-interop-evidence-kit-ko.md. Фактическая завершённая совместимость учитывается только для тех объёмов, где есть запись install → restart → tool list → check_my_app → reconnect для каждого клиента, уникальный SHA-256 и отдельная проверка.
Вызовы MCP также по умолчанию применяют лимиты на аргументы и встроенный текст:
K_GUARD_MCP_MAX_FILES=1000K_GUARD_MCP_MAX_MB=10K_GUARD_MCP_MAX_TEXT_MB=1K_GUARD_MCP_MAX_ARG_CHARS=4096K_GUARD_MCP_GUARDIAN_MAX_TARGETS=100K_GUARD_MCP_GUARDIAN_MAX_REQUESTS=200
Вызовы Guardian MCP предварительно проверяют весь манифест на соответствие бюджетам целевых объектов, отфильтрованных файлов, байтов и прогнозируемых HTTP-запросов. Расширение файлов ограничено настроенным лимитом плюс одна запись обнаружения, поэтому слишком большое дерево отклоняется без предварительного перечисления всего дерева. Чрезмерно большая работа с несколькими целями возвращает отчёт Guardian с закрытым отказом; для намеренно больших пакетов используйте CLI или разделите манифест. Динамическая HTTP-проверка по умолчанию читает не более 1 МиБ на ответ, а также останавливается на 16 МиБ, 80 запросах или 30 секундах на один прогон зонда.
Безопасность по умолчанию
Выводы JSON/Markdown/MCP проходят через централизованный слой редактирования
Вывод SARIF также проходит через тот же слой редактирования и хранит доказательства без сырых данных
Динамический зонд по умолчанию разрешает только
localhost,127.0.0.1и::1Внешний динамический зонд требует явных доказательств авторизации и согласия, затем остаётся с фиксированными путями, только для чтения, с тем же источником и без перенаправлений. Каждый запрос разрешает только разрешённые публичные адреса и подключается к выбранному проверенному IP, сохраняя исходный HTTP Host/TLS SNI.
Динамический зонд не следует перенаправлениям на неразрешённые хосты
Динамический зонд использует только неразрушающие HTTP-методы
--deep-active— это ограниченная авторизованная проверка распространённых открытых путей.env,.git/config, резервных копий/дампов и отладочных/рантайм-путей. Это не рекурсивное сканирование, не подбор паролей, не эксплойт-пейлоады и не словарный перебор путей.Динамический зонд использует таймаут и ограничивает выборку тел ответов
Проверка внешних секретов на действительность не выполняется
Серьёзность и уверенность
Значения серьёзности проверяются централизованно:
critical: раскрытие учётных данных/приватных ключей/строк подключения к БД, прямые высокорисковые идентификаторы или неаутентифицированные чувствительные маршрутыhigh: сильно идентифицирующие комбинации, рискованное локальное раскрытие или кандидаты на чувствительные потокиmedium: отдельные сигналы контактов/местоположения/конфигурации и находки по усилению защитыlow: слабые эвристические сигналыinfo: информационные наблюдения
Значения уверенности: high, medium и low.
Поток данных и ограничения внутреннего ядра
Карта потока явно помечена как ЭКСПЕРИМЕНТАЛЬНАЯ и обозначена как heuristic+ast-taint+js-ts-taint с точностью line-distance+python-intra-procedural-ast+js-ts-intra-file+limited-interprocedural-taint.
Это инструмент для триажа разработчика: поддерживается Python AST-taint, а JS/TS теперь имеет лёгкий внутрифайловый taint, ограниченные сводки маршрут-к-сервису, а также эвристики с учётом фреймворков для обработчиков маршрутов/серверных действий Next.js, IDOR-образных параметров маршрутов Express, подсказок границ Supabase service-role/RLS и подсказок границ аутентификации Firebase Admin. Полная проверка типов TypeScript, разрешение импорта с учётом алиасов, доказательство middleware, доказательство развёрнутых правил RLS/Firebase и полный межпроцедурный taint остаются пунктами дорожной карты.
Находки потока включают хэши строк источника/приёмника без сырых данных, чтобы сделать доказательства воспроизводимыми без раскрытия чувствительных значений.
Визуализации потока генерируются как локальные SVG/HTML без внешних JavaScript или CDN-зависимостей. Тот же слой редактирования защищает JSON, Markdown, SVG, HTML и ответы MCP.
Наблюдение MCP в рантайме принимает экспорт событий JSONL/JSON через observe_mcp_events и может также оценивать одно событие за раз через observe_mcp_event(session_id=...). CLI mcp-intercept применяет решения block и redact к пакетному потоку событий перед записью пересылаемого JSONL. CLI mcp-proxy -- <upstream argv> — это путь принудительного применения в реальном времени для построчного stdio JSON-RPC. CLI mcp-http-proxy --upstream <URL> применяет ту же политику к жизненным циклам сессий Streamable HTTP POST, GET SSE и DELETE, с официальной поддержкой совместимости Python MCP SDK. Оба прокси излучают одну и ту же цепочку квитанций HMAC без сырых данных, связанную с транзакциями, для решений об отказе в доступе и разрешении/редактировании/блокировании контента; настроенная сохранность в сайдкаре происходит перед пересылкой и завершается отказом. Проверка секрета оператора требуется для заявлений об устойчивости к вмешательству, потому что HMAC — это тег с общим секретом, а не подпись с открытым ключом. Оба прокси завершаются отказом при неверной структуре кадров, ошибках корреляции, превышении размера или таймаута, а также при сбоях управления транспортом. Состояние потокового наблюдателя имеет ограниченное удержание сессий idle/LRU. MCP enforce_mcp_events остаётся только для отчётов. Бинарная структура кадров, нестандартные транспорты и универсальный прокси для всех MCP-клиентов остаются работой на будущее.
Коннекторы только для чтения проверяют только локальные файлы SQLite/логов/хранилищ. Удалённые Postgres/Supabase/Firebase/S3, резервные копии и фактическое выполнение удаления — пункты дорожной карты.
Цикл обратной связи по дрейфу
k-guard feedback --type fn --rule PII_PHONE --text "missed sample" --output feedback.jsonl
k-guard feedback-export --input feedback.jsonl --output feedback-summary.json --reviewedФайлы обратной связи очищаются перед записью и обобщаются локально для настройки корпуса. Фрагменты ложноотрицательных результатов используют дополнительное консервативное маскирование токенов, потому что детектор мог пропустить чувствительное значение.
feedback-export требует --reviewed или K_GUARD_FEEDBACK_EXPORT_REVIEWED=1, чтобы случайные автоматические экспорты завершались отказом. Просмотрите feedback-summary.json перед передачей за пределы партнёрской среды; не передавайте сырой feedback.jsonl.
Полевой бенчмарк
benchmark-template создаёт манифест 20/20/10:
20 общих базовых сайтов или отчётов
20 подозрительных сайтов или отчётов, созданных с помощью vibe-coding
10 собственных, одобренных партнёром или находящихся в рамках bug-bounty целей
benchmark агрегирует отчёты K-Guard JSON без сырых данных и, только с --run-probes, авторизованные строки манифеста с пометкой mode=probe. Он сообщает долю целей с высоким/критическим уровнем, долю обнаружения сильных идентификаторов, основные правила и долю ложных срабатываний вручную по когортам. По умолчанию путь — только отчёт, поэтому работу над бенчмарком можно начать с экспорта дашборда, не касаясь внешних сайтов.
Тот же рабочий процесс доступен через MCP как create_benchmark_template и field_benchmark. Зонды бенчмарка MCP требуют тех же явных согласий на зондирование, что и probe_http.
Режим Guardian
guardian-template создаёт авторизованный манифест целей для непрерывной защиты продукта. Строка может указывать на локальное рабочее пространство, HTTP-источник, экспорт JSONL рантайма MCP или существующий отчёт K-Guard JSON без сырых данных. guardian агрегирует все строки в один отчёт без сырых данных с воротами для каждой цели, дрейфом новых/решённых находок, основными повторяющимися правилами и рецептами исправлений из suggest_fix.
Манифесты Guardian также содержат поля бизнес-намерений и объёма без сырых данных: business_purpose, data_classes, user_scope, public_endpoints, scope_basis и scope_proof_ref. K-Guard не претендует на понимание бизнеса на уровне человека и не доказывает владение сам по себе; он записывает, существуют ли эти утверждения, и хранит только хэшированные ссылки. Завершение объёма требует явного scope_proof_ref; локаторы и устаревшие заметки об авторизации не считаются доказательством. Ворота выпуска данных требуют этих контрактов на уровне целей и проверенных пакетов доказательств, подписанных оператором, чьи хэши артефактов совпадают с текущим отчётом, прежде чем считать отчёт доказательством отгрузки.
Новые шаблоны устанавливают audit_profile=korean_senior. В этом профиле ворота выпуска не проходят только потому, что не найдено блокирующих проблем. Также требуется содержательное рабочее пространство с хотя бы одним поддерживаемым и успешно декодированным производственным исходным файлом, анализ потока, завершённый глубокий HTTP-обзор, объявленные пути конечных точек, которые фактически возвращают 2xx/3xx/401/403, а не только 404, и явные утверждения о назначении/данных/пользователях/объёме. Guardian вычисляет хэш содержимого каждого рабочего пространства непосредственно до и после аудита; несовпадение вызывает GUARDIAN_SOURCE_CHANGED_DURING_AUDIT, блокирует цель и исключает этот запуск из содержательного покрытия. Используйте неизменяемый CI-чекаут для авторитета выпуска. Отчёт раскрывает review_contract.domains для site_security, api_exposure, data_management и operational_risk; guardian_gate.passed остаётся ложным, когда любая требуемая область неполна. Устаревшие манифесты без audit_profile остаются на более узком профиле standard и не должны представляться как четырёхдоменное старшее покрытие.
public_endpoints — это исполняемый объём проверки, а не декоративные метаданные. Guardian безопасно добавляет допустимые пути с тем же источником из этого поля в фиксированный набор зондов GET/OPTIONS (максимум 50; без абсолютных URL, обхода родительских каталогов или шаблонных путей). Это позволяет проверке API охватывать специфичные для приложения конечные точки только для чтения, сохраняя зонд ограниченным.
Когда HTTP-строка включает session_file, Guardian выполняет тот же ограниченный набор путей дважды: один раз без аутентификации и один раз с предоставленной оператором сессией только для чтения. Это сохраняет проверки публичного раскрытия видимыми, а не заменяет их представлением только с аутентификацией. Сессия должна доказать как переход через аутентификационный барьер, так и дайджест ответа объявленной личности. Ворота korean_senior отклоняют HTTP-покрытие, если отсутствует любое из доказательств, если зонд вызвал ошибки запроса или управления, или если не был достигнут допустимый объявленный объём конечных точек.
guardian --fail-on high — это канонические ворота перед выпуском. Это рабочий процесс для случаев, когда K-Guard должен решить, может ли целевое приложение быть выпущено при заданном пороге. Только рабочее пространство scan --fail-on и MCP security_gate — это быстрые проверки; они не заменяют контракт Guardian на манифест, пробелы покрытия, дрейф и выполнение с несколькими целями.
По умолчанию guardian записывает отчёт и завершается успешно, чтобы команды могли просмотреть находки, не превращая его в блокировку выпуска. Используйте --fail-on high, когда тот же отчёт должен действовать как CI-ворота; он завершается с кодом 3, когда присутствуют блокирующие цели или новые блокирующие находки. При первом запуске без --previous отчёт является initial_snapshot: текущие блокировки и пробелы покрытия всё ещё могут провалить ворота, но K-Guard не помечает каждую текущую находку как новый дрейф. В режиме ворот HTTP-строки в манифесте должны фактически выполняться через --run-probes, иначе они становятся пробелами покрытия, а не молчаливыми проходами.
HTTP-строки не выполняются, если не передан --run-probes. Путь CLI предназначен для доверенного локального оператора и использует явные флаги CLI/строки манифеста как механизм opt-in. MCP опосредован агентом и строже: guardian_audit(..., run_probes=true) требует K_GUARD_MCP_ENABLE_PROBE=1; манифесты, запрашивающие внешние, глубоко-активные или сессионные пробы, дополнительно требуют K_GUARD_MCP_ENABLE_EXTERNAL_PROBE=1, K_GUARD_MCP_ENABLE_DEEP_ACTIVE_PROBE=1 или K_GUARD_MCP_ENABLE_SESSION_PROBE=1 соответственно. Режим Guardian по-прежнему использует фиксированные безопасные GET/OPTIONS-пробы и не выполняет попытки входа, подбор паролей, мутации, эксплойт-пейлоады, рекурсивное сканирование или переходы по редиректам на другие хосты.
MCP-клиенты должны передавать fail_on="high" в guardian_audit для канонического вердикта release-gate в guardian_gate.passed; коды выхода процесса остаются заботой CLI. Используйте --previous previous-guardian-report.json, чтобы превратить отчёт в монитор дрейфа. Новые блокирующие находки перечисляются отдельно только при загрузке предыдущего отчёта; комбинируйте --previous с --fail-on, когда эти находки должны остановить релиз. Если MCP-контрольные проверки останавливают guardian до выполнения цели, и режим отчёта, и режим gate возвращают ответ в форме guardian с execution_contract, summary и findings; режим gate дополнительно включает guardian_gate.passed=false. Отчёт включает секцию execution_contract, чтобы пропущенные, ошибочные и выполненные цели были видны в одном месте, включая экспорт в Markdown и HTML.
MCP security_gate(path, fail_on) — это быстрый gate только для рабочего пространства. Теперь он возвращает security_gate.passed=false при сбоях MCP-контроля, таких как недопустимые пороги, бюджеты аргументов, лимиты бюджета рабочего пространства или исключения при сканировании, но намеренно сообщает coverage_model="workspace_only". Используйте Guardian для решений о релизе, которым нужны HTTP-цели, MCP-экспорт времени выполнения, предыдущие отчёты или учёт пробелов покрытия.
Политика подавления — fail-closed. suppression-template создаёт CSV, который привязывает каждый отказ от проверки к app_id, audit_profile, текущему снимку исходников или хэшу review-evidence, target_id, отпечатку находки, владельцу, причине и будущему сроку истечения. Отказ от проверки не может быть воспроизведён для другого приложения или изменённого снимка релиза; недопустимые, устаревшие или истёкшие строки добавляют policy-находку высокой серьёзности. Подавления не могут очистить пробелы покрытия Guardian или правила сбоев MCP-контроля. field-validation-queue экспортирует каждого кандидата high/critical с редактированным отпечатком, подтипом детектора, областью артефакта, типом расположения файла/тела/заголовка и доступным хэшем ответа. Строгое утверждение field-validation-report --profile field остаётся закрытым, пока 12–20 собственных/партнёрских приложений, 120 размеченных случаев, 50 положительных, 40 отрицательных, пять critical-случаев, три страты, предварительно зарегистрированный holdout как минимум с 70 случаями, 35 положительными, 35 отрицательными и 35 кандидатами, разделённые по ролям подписи рецензента/хранителя и точный второй канонический прогон Guardian не пройдут все фиксированные пороги. Общая/holdout точность, holdout high/critical полнота и holdout специфичность должны пройти как точечную оценку, так и нижнюю границу Wilson 95%; critical holdout полнота остаётся ровно 1.0. Каждый кандидат должен быть привязан ровно к одной замороженной области разработки/holdout. (false_positive + benign) / (true_positive + false_positive + benign) кандидата сообщается как доля ложных обнаружений; доля ложных положительных результатов рассчитывается только по чистым случаям как FP / (FP + TN). Порог выпуска данных строже: любое известное ложноотрицательное или доброкачественное high/critical-кандидат блокирует выпуск. K_GUARD_FIELD_REVIEWER_HMAC_KEYS и K_GUARD_FIELD_CUSTODIAN_HMAC_KEY должны отличаться от ключа операторских доказательств; эти локальные HMAC разделяют роли, но не доказывают реальную идентичность или криптографически независимое выполнение. Устаревшие команды validation-* агрегируют только метки кандидатов и не могут установить полевую полноту.
Публичный контракт квалификации автоматических блокировок отделён от полевой валидации. release_blocker_actionability_v3 требует предварительно зарегистрированную перепись как минимум 100 автоматических блокировок, 0.90 действенности кандидатов и нижнюю границу Wilson 95% 0.80. Он также требует автоматические блокировки как минимум в 20 приложениях. Приложение считается полностью действенным только тогда, когда каждая автоматическая блокировка в этом приложении помечена true_positive; и доля полностью действенных приложений, и нижняя граница Wilson должны пройти 0.90 и 0.80. Этот порог чувствительности на уровне приложений предотвращает создание ложной статистической уверенности десятками коррелированных строк в одном репозитории. Ручные проверки и удержания целостности политики остаются fail-closed, но исключены из действенности автоматических блокировок.
Полный контракт уровней доказательств, первого базового уровня, разметки и повторных прогонов задокументирован в docs/field-validation-v2-ko.md.
data-release-gate строже: он требует K_GUARD_EVIDENCE_HMAC_KEY, оригинальный манифест Guardian, каноническое утверждение Guardian уровня high, привязанное к текущему отчёту, снимки доказательств целей и исходников, отдельно подписанные первичные и повторные оригиналы Guardian валидационных исходников, точные CSV ground-truth/рецензента, оригинальную предварительную регистрацию хранителя, доказательства на корейском корпусе и фактический пересылаемый поток MCP-перехватчика. Он повторно запускает полевую валидацию из этих оригиналов, повторно проверяет HMAC рецензента/хранителя и требует, чтобы пересчитанная проекция равнялась представленному отчёту. Он также повторно открывает повторный Guardian и независимо проверяет контент, пакет, аттестацию выполнения, инструментарий, манифест, входные данные source/HTTP/MCP и точный мультинабор кандидатов. Валидация дизайн-партнёра намеренно независима от единственного приложения, которое в настоящее время выпускается; ссылки на приложение/цель/находку кандидата должны соответствовать отчёту Guardian валидационных исходников, а не маскироваться под покрытие выпускаемого приложения. Многоразовый шаблон GitHub Actions находится в docs/templates/github-actions/guardian-release-gate.yml.
Неаутентифицированный JSON разделяется по риску. Структура чувствительных/приватных записей остаётся DYN_UNAUTH_API_JSON с высокой серьёзностью; JSON без этих индикаторов становится DYN_PUBLIC_API_JSON_REVIEW с информационной серьёзностью, чтобы намеренный публичный API не блокировал выпуск только за возврат JSON.
scripts/site_scale_calibration.py запускает локальные black-box HTTP-фикстуры для большого общего HTML-шума, средних публичных контактных страниц, малого bulk-JSON в стиле vibe-coding, валидных сильных идентификаторов и JSON банковских счетов. Он не использует внешний сетевой доступ.
scripts/deep_probe_synthetic_calibration.py запускает локальный синтетический loopback-калибровочный стенд через тот же путь dashboard scan_url(..., deep_active=True). Прогон доказательств по умолчанию использует 500 loopback-вызовов в 24 ограниченных сценариях; это не 500 реальных веб-сайтов, не обнаружение уязвимостей и не пентестинг. Он покрывает .env, .git/config, резервные SQL, отладочные конечные точки, экспозицию admin/API, OpenAPI, source maps, CORS, редиректы, уровни ответов PII/секретов и контроли ложных срабатываний для login-стен, SPA-оболочек и HTML-фолбэков. Он также включает отрицательные контроли, доказывающие, что скорер «ожидаемое против наблюдаемого» может обнаруживать внедрённые отсутствующие и неожиданные правила. Он не использует внешний сетевой доступ; реальные внешние глубокие пробы по-прежнему требуют явной авторизации цели.
scripts/inner_core_product_gate.py запускает более широкий локальный синтетический продуктовый gate для утверждения аудита «inner-core». Он объединяет 500-целевую loopback-калибровку глубоких проб со статическими проверками кода/конфигурации/MCP-текста, Python AST-taint, наблюдением MCP-рантайма JSONL, коннекторами SQLite/журналов/хранилищ только для чтения, проверкой хранения/удаления, вердиктами PII-к-агентному/внешнему на разных уровнях и проверками графа доказательств без сырых данных. Этот gate должен иметь ноль отсутствующих обязательных правил, ноль неожиданных правил глубоких проб, проходящие отрицательные контроли, непустые узлы/рёбра графа потока и отсутствие запрещённых сырых маркеров в сериализованном отчёте. Это gate глубины продукта, а не доказательство обнаружения уязвимостей сторонних сайтов.
CI-рабочий процесс запускает полный набор pytest плюс 24-целевой inner-core gate при push/pull request, который покрывает каждый синтетический сценарий один раз. Планируемый/ручной аудит-рабочий процесс запускает 500-целевой inner-core gate и загружает пакет доказательств JSON/Markdown/HTML. Само-SARIF-сканирование репозитория загружается как неблокирующее доказательство, потому что этот репозиторий сканера намеренно содержит правила детекторов, фикстуры и примеры документации, которые должны вызывать находки K-Guard; используйте --fail-on против аудируемого продукта, а не против собственного дерева исходников K-Guard с обилием фикстур, если не существует проектного базового уровня.
scripts/passive_homepage_calibration.py запускает калибровку только главной страницы GET / из CSV-манифеста. Он поддерживает --max-targets, --delay-ms, --checkpoint-jsonl и --resume для шардированных прогонов масштаба 10k. Он сообщает агрегаты когорты, рангового сегмента, исхода и уровня гигиены, такие как well_managed_quiet, hardening_gap, boundary_redirect и messy_or_risky_signal, чтобы шумные хорошо управляемые сайты и беспорядочные длиннохвостые сайты можно было калибровать отдельно. Он также выдаёт release_gate с проверками pass/warn/fail для выхода измерений, доли high/critical, доли сильных идентификаторов и доли boundary-редиректов. Он не запрашивает /admin, /api, .env, .git, OPTIONS или рекурсивные пути.
scripts/build_top_domain_manifest.py автоматически создаёт этот пассивный калибровочный манифест из публичного CSV или ZIP-источника топ-доменов. Источник по умолчанию — последний top-1m ZIP Tranco, поэтому операторам не нужно вручную собирать 10 000 URL. Используйте --start-rank для создания когорт длиннохвостых беспорядочных кандидатов, например ранги с 900 001 по 910 000. CSV в стиле Majestic Million также можно указать с помощью --source-url.
Доказательства RC для конкурса только с ИИ
Конкурсный RC хранит доказательства разработки в отдельных дорожках вместо публикации одного объединённого показателя точности:
python scripts/qualify_korean_privacy_ai_only.py --output evidence/qualification/korean-privacy-ai-only-v1.json
python scripts/ai_public_benchmark_scorecard.py --output evidence/qualification/ai-public-benchmark-scorecard-v1.json
python scripts/benchmark.py --profile contest --output benchmark-report.jsonКорейский квалификационный отчёт сообщает 117 фикстур-случаев и замороженный holdout из 68 случаев, написанный оценщиком, как отдельные дорожки. Он также проверяет пять контрактов рабочего пространства, четыре официальных концепции уникальных идентификаторов, границы бизнес/корпоративных номеров и шесть поверхностей чувствительной лексики. Он не публикует объединённую матрицу ошибок и не заявляет о слепой, подтверждённой человеком, реестровой, партнёрской или полевой точности.
Публичная карта показателей сохраняет текущие и исторические дорожки отдельно.
--require-integrity-passнамеренно завершается ошибкой, пока любой выбранный исторический артефакт имеет несоответствие дайджеста или привязки; отчёт всё равно может быть сгенерирован, чтобы недопустимый исторический результат оставался видимым, а не молча переписывался или удалялся.Профиль производительности конкурса измеряет точные синтетические all-benign низкосигнальные корпуса на одном раскрытом хосте, включая холодную задержку свежего процесса и тёплую задержку постоянного сканера, масштабирование 10/50/100 МиБ, конкурентность потоков CPython в процессе 1/4/8, пиковый RSS, полное покрытие кандидатов и инвариантность результатов без сырых данных. Он не измеряет масштабирование с плотностью находок, параллелизм на уровне процессов, производственный SLO, полевую точность, сравнение с нормализацией по оборудованию или превосходство третьих сторон.
Текущие ограничения
Дорожная карта усиления продукта: docs/pre-release-auditor-hardening-plan-ko.md
Извлечение PDF/DOCX/HWP пока не включено
Динамические проверки выполняются только на localhost, если только они явно не внесены в белый список или не подтверждены оператором для собственной, одобренной партнёром или охваченной программой bug-bounty внешней цели
Предоставленные пользователем заголовки сеанса прикрепляются только к запросам GET, доступным только для чтения, и значения заголовков не сохраняются
Taint-анализ JS/TS является консервативным и лёгким, с внутрифайловым taint-анализом, ограниченными сводками маршрут-к-сервису и эвристиками с учётом фреймворков для распространённых ошибок на границах Next.js/Express/Supabase/Firebase; taint-анализ AST Python является внутрипроцедурным
Наблюдение за MCP во время выполнения и инструмент MCP
enforce_mcp_eventsостаются рекомендательными;mcp-proxyиmcp-http-proxyвыполняют фактическое двунаправленное принуждение для построчного stdio JSON-RPC и протестированного жизненного цикла Streamable HTTP. Двоичная кадровая структура, нестандартные транспорты и универсальный прокси-заменитель для каждого клиента остаются за пределами принудительной границыУдалённые БД/хранилища/резервные копии и фактическое выполнение удаления пока не проверяются
Корейские PII-шаблоны основаны на правилах и требуют настройки ложных срабатываний для конкретного проекта
Корейские регистрационные номера предприятий используют проверку синтаксиса контрольной суммы на синтетических/созданных вендором фикстурах. Исторические корпоративные регистрационные номера (выданные до 2025-01-31) могут использовать старую контрольную сумму Приложения с чередующимися весами 1,2. С 2025-01-31 текущие корпоративные номера состоят из 4 цифр реестра + 2 цифр типа + 7 серийных цифр без контрольной суммы, поэтому обнаружение — это только явное распознавание контекста поля/синтаксиса. Ничто из этого не является PII физического лица, проверкой живого реестра или независимой проверкой поля. Текущие значения только по контексту — это непроверенное распознавание синтаксиса/контекста. Неоднозначность в пользу конфиденциальности: правдоподобный корейский RRN/FRN имеет приоритет над корпоративной или деловой меткой, заголовком или разделённым JSON-ключом и никогда не классифицируется и не редактируется как идентификатор организации.
Созданный оценщиком набор из 68 корейских чувствительных/организационных случаев (
evidence/holdout/korean-sensitive-org-v1.cjson) — это проверка синтетических оракулов после внедрения, а не слепое исследование точности полей или проверки реестра. Случай проходит, когда присутствует каждое правилоmust_all, каждая группаmust_anyимеет хотя бы одно соответствующее правило и не появляется ни одно правилоforbidden(показ recall по любому правилу для каждого случая). После этого прохода замороженный манифест набирает 68/68 с recall 1.0, специфичностью 1.0 и точным повторением двух прогонов. Это не заявление о точности полей в реальном времени.
Available Tools
28 toolsbuild_flow_mapC
Build an EXPERIMENTAL heuristic data-flow risk map for source and sink patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. It only labels the tool as 'EXPERIMENTAL heuristic', which hints at unreliability but does not describe side effects, return behavior, or whether it is read-only. This is insufficient for a tool that might have complex outputs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence with no wasted words. It is front-loaded with the action and subject, making it easy to parse. However, it is minimal to the point of missing critical details, which slightly lowers the score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides only a high-level purpose and fails to explain the parameter, expected output, or any operational context. Given the lack of annotations and output schema, this is severely incomplete for an agent to call the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description does not mention the 'path' parameter at all. There is no explanation of what 'path' should point to or its format, leaving the agent completely uninformed about the only required input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool builds a data-flow risk map for source and sink patterns, using a specific verb and resource. It is not a tautology and is distinguishable from sibling audit tools, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus others, nor any exclusions or alternative tools. The single-sentence description offers no contextual triggers or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_my_appBRead-only
Review the complete bound workspace; poll with continue_review until terminal.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | 검사할 프로젝트 경로. 설치 시 고정된 워크스페이스에서는 '.'을 사용합니다. | . |
| include_flow | No | 소스에서 입력·처리·외부 전송 흐름 분석을 함께 실행합니다. |
Output Schema
| Name | Required | Description |
|---|---|---|
| method | Yes | |
| findings | No | |
| experience | Yes | |
| review_job | No | |
| guardian_gate | No | |
| review_receipt | No | |
| primary_workflow | No | |
| application_assurance | No | |
| auditor_qualification | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the polling behavior, which is genuinely useful beyond annotations, but it does not explain what 'terminal' means, what the response contains, or any side effects (e.g., state changes between polls). Since it adds some behavior context but not full transparency, a 3 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no filler. The core purpose ('Review the complete bound workspace') is front-loaded, and the polling instruction is a necessary operational detail. It could arguably be more structured (e.g., listing return behavior), but it is appropriately concise for the amount of information it conveys.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a rich output schema (which likely explains the 'terminal' state and review results), so the description does not need to detail return types. However, the phrase 'until terminal' is ambiguous without context on how to recognize terminality, and the description does not clarify the scope of 'complete bound workspace' or what 'review' entails (e.g., security, code quality, dependencies). Given the complexity of a polling-based review, the description is minimally complete but leaves gaps that the schema and sibling definitions may or may not fill.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents both parameters fully (100% coverage), including the meaning of 'path' ('checks the project path; use '.' for the fixed workspace') and 'include_flow' (whether to run input/processing/external transmission flow analysis). The description adds nothing about parameters beyond what the schema provides, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a clear verb ('Review') and a resource ('the complete bound workspace'), so an agent can deduce the tool's core function. It also hints at a distinctive workflow (polling with continue_review) that sets it apart from simpler scans, but it doesn't explicitly name or contrast siblings, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The instruction 'poll with continue_review until terminal' gives a usage pattern, implying this is a long-running operation and that continue_review is the follow-up tool. However, it does not state when to prefer this over alternatives like scan_workspace or start_review_before_ship, nor does it give exclusions or prerequisites. The guidance is implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
continue_reviewARead-onlyIdempotent
Wait briefly for a long review; repeat until state is completed or failed.
| Name | Required | Description | Default |
|---|---|---|---|
| review_id | Yes | check_my_app 또는 start_review_before_ship이 돌려준 검수 번호 | |
| wait_seconds | No | 한 번에 기다릴 시간(0~4초). 완료될 때까지 이 도구를 반복 호출합니다. | |
| finding_limit | No | 완료 결과에 포함할 findings 수(1~50) | |
| response_mode | No | compact(기본) 또는 full(호환용 전체 결과) | compact |
| finding_offset | No | 완료 결과 findings 페이지 시작 위치 |
Output Schema
| Name | Required | Description |
|---|---|---|
| method | Yes | |
| findings | No | |
| experience | Yes | |
| review_job | No | |
| guardian_gate | No | |
| review_receipt | No | |
| primary_workflow | No | |
| application_assurance | No | |
| auditor_qualification | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context about the iterative waiting mechanism and the terminal states, which goes beyond what annotations declare. This clarifies that multiple calls may be needed and defines the loop exit condition without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that captures the essential behavior and stop condition. It contains no redundant details, and for a tool with 5 parameters, the schema fully covers parameter specifics, so brevity is both appropriate and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description conveys the core polling loop and terminal decisions, sufficient for an agent to understand the call pattern. With annotations covering safety and schema covering parameters, the description need not list parameters or response format. It could be more explicit about error handling or max retry expectations, but for a standard polling tool this is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed per-parameter descriptions (e.g., wait_seconds limits, finding_limit range), so the description adds no parameter-level value. As per baseline for high schema coverage, a score of 3 is appropriate since the description effectively delegates parameter comprehension to the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Wait briefly') and a specific resource ('a long review') with an explicit termination condition ('until state is completed or failed'). It distinguishes itself from sibling start_review_before_ship by being the polling/waiting counterpart, making its role unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a polling loop but does not explicitly state when to call it, such as after start_review_before_ship or check_my_app. It lacks any mention of alternative tools or exclusion criteria, leaving preparation and sequencing to caller inference. The schema's review_id description helps but is not part of the tool description itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_benchmark_templateC
Create a 20/20/10 field benchmark manifest CSV template.
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only says 'Create' without disclosing side effects, such as whether it overwrites an existing file, what the return value looks like, or any permission requirements. The description adds no behavioral context beyond the trivial fact of creation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no waste. However, it is too terse, sacrificing necessary detail for brevity. Front-loading is acceptable, but the cryptic '20/20/10' and lack of any explanation reduce its effectiveness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no annotations, the description should at least explain what a 'benchmark manifest' is, whether the file is created or overwritten, and any output expectations. The description lacks these details, making it incomplete for an agent to use correctly without prior domain knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has only one parameter, output_path, with zero description coverage. The description does not mention the parameter at all, so an agent has no information about the expected format, whether it's a directory or full path, or the file name conventions. The description fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Create') and a specific resource ('20/20/10 field benchmark manifest CSV template'). The 'benchmark' keyword distinguishes it from sibling template tools like create_field_campaign_template and create_guardian_manifest_template. It is specific but leaves the meaning of '20/20/10' unexplained, so not a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the sibling template tools. There is no mention of alternatives, exclusions, or selection criteria. An agent would have to infer usage from the name alone, which is not sufficient when similar templates exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_field_campaign_templateC
Create the empty 12-20 app owned/partner roster used before holdout preregistration.
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for disclosing behavioral traits. It only says 'create', which is generic and matches the tool name; it does not mention whether the operation is destructive, whether it overwrites existing files, what permissions are required, or what success/error responses look like. The description adds minimal value beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (a single sentence) with no wasted words. However, it is so terse that it omits essential details, making it under-specified rather than efficiently structured. It is neither bloated nor well-organized; it simply lacks substance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter, no output schema, and no annotations, the description should at least clarify what 'empty roster' means, what output_path should point to, and any prerequisites or side effects. The description only gives a vague workflow hint ('before holdout preregistration') and leaves the main operational details undocumented, making it insufficient for a correct call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no parameter descriptions (coverage 0%) and the tool description does not mention the 'output_path' parameter at all. Even though the parameter name is somewhat self-explanatory, the description fails to elaborate on expected format, path conventions, or default behavior. The description offers zero assistance for correct parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Create') and a specific resource ('empty 12-20 app owned/partner roster'), and gives a context cue ('before holdout preregistration'). It is clear about what the tool does, though it does not explicitly contrast with sibling template creators like create_benchmark_template or create_guardian_manifest_template. The purpose is distinguishable by the unique resource name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'used before holdout preregistration' implies a temporal context but does not give explicit when-to-use vs when-not-to-use guidance, nor does it mention any alternative tools or conditions for choosing this tool over a sibling. There is no guidance on preconditions or expected workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_guardian_manifest_templateC
Create a korean_senior Guardian manifest for four-domain pre-release reviews.
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing side effects. It says 'create' but does not describe whether the tool overwrites existing files, requires specific permissions, or produces a specific return value. The lack of behavioral detail (e.g., idempotency, error conditions) leaves the agent without important operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that is front-loaded with the action. It is concise without padding, but its brevity comes at the cost of missing critical details, making it more under-specified than efficiently concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool that creates a file, with no annotations, no output schema, and no parameter documentation, the description is far from complete. It omits details about the manifest structure, default behavior, and parameter semantics, which an agent would need to invoke the tool correctly and safely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining the output_path parameter. It does not: the description only mentions the manifest creation and gives no guidance on the expected format, constraints, or usage of output_path. The parameter name is self-explanatory but the description adds no additional semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Create' and the resource 'a korean_senior Guardian manifest', and adds the specific context of 'four-domain pre-release reviews.' This distinguishes it from sibling template tools like create_benchmark_template and create_field_campaign_template, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for pre-release reviews but does not explicitly state when to use this tool versus similar template creators, nor does it mention any exclusions or alternatives. An agent would have to infer the use case from the name and description, lacking explicit routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
data_release_gateC
Run the strict data shipment gate over Guardian, validation, Korean corpus, and MCP interceptor evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| guardian_report_path | Yes | ||
| guardian_manifest_path | Yes | ||
| validation_report_path | Yes | ||
| validation_review_path | Yes | ||
| validation_roster_path | Yes | ||
| korean_corpus_report_path | Yes | ||
| mcp_forwarded_output_path | Yes | ||
| mcp_intercept_report_path | Yes | ||
| korean_fixture_corpus_path | Yes | ||
| validation_ground_truth_path | Yes | ||
| validation_preregistration_path | Yes | ||
| max_validation_false_positive_rate | No | ||
| validation_repeat_guardian_report_path | Yes | ||
| validation_source_guardian_report_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden of behavioral disclosure. It only says 'Run' the gate, without stating whether it is read-only, destructive, requires specific inputs, or what happens on failure. With 14 parameters and no cautions, the agent has no idea of side effects or prerequisites—a serious gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the action and lists evidence categories. There is no filler or redundancy. However, given the tool's complexity, it is overly terse, though conciseness itself is well-handled.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 14 required parameters, no annotations, no output schema, and no parameter descriptions, the description is grossly incomplete. It fails to explain what each path is for, what the gate checks, success criteria, side effects, or output format. An agent cannot confidently invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It groups evidence into Guardian, validation, Korean corpus, and MCP interceptor categories, which helps map high-level intent but does not explain the 14 specific path parameters, their expected formats, or relationships. This is insufficient for an agent to correctly populate all required fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb 'Run' and resource 'strict data shipment gate', and lists the evidence sources (Guardian, validation, Korean corpus, MCP interceptor). It is clear what the tool does overall, though it does not explicitly differentiate from sibling tools like security_gate or guardian_audit. The distinctive evidence components give some differentiation, hence a 4.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. It omits prerequisites, exclusions, or context clues such as 'use this before shipping data' or 'do not use if X'. The agent must infer usage from the tool name and stack. This is a significant miss for a tool with many related siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
deep_analyzer_auditC
Run the local fail-closed Semgrep deep profile; this is intrafile evidence, not an authorization proof.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only discloses a few behavioral traits: 'fail-closed' and 'intrafile evidence, not an authorization proof'. These are useful, but it does not mention permissions, side effects, rate limits, or output behavior. It adds some context beyond the schema but leaves many aspects undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—one sentence with two clauses—and front-loads the action. Every word earns its place, but the brevity leaves essential details missing, so it is efficient but not optimally informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a security audit tool with only one parameter and no output schema, the description is incomplete. It does not explain the return value, the exact meaning of 'workspace_path', prerequisites, or how the result relates to other tools. The caveats about fail-closed and non-authorization are helpful, but the agent lacks critical operational details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the parameter 'workspace_path', but it provides no explanation of what this parameter expects, its format, or its purpose. The agent has no additional information beyond the variable name, making it difficult to invoke correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Run the local fail-closed Semgrep deep profile') and a clear resource (Semgrep deep profile), with an additional qualifier about its scope ('intrafile evidence, not an authorization proof'). It is clear about what the tool does, though it does not explicitly differentiate from sibling tools like scan_workspace or software_composition_audit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The phrase 'intrafile evidence' hints at a single-file analysis scope, but it does not state conditions or mention sibling tools. No exclusions or alternative routing is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
enforce_mcp_eventsC
Evaluate MCP runtime block/redact policy and return a raw-free report-only interceptor result.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| label | No | mcp-runtime-events |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry all behavioral disclosure. It mentions 'report-only' (non-destructive) and 'raw-free' (does not expose raw data), which are helpful, but it does not clarify whether any state is modified, whether authentication is needed, or the exact nature of the interceptor result. The disclosure is partial despite carrying the full burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with the primary action front-loaded. It is concise and avoids filler, though it may be too terse for the tool's complexity. In terms of structure, it communicates the core action efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, minimal parameter documentation, and a relatively complex policy-evaluation task, the description is severely incomplete. It fails to explain the interceptor result, parameter semantics, or any side effects, leaving the agent without enough context to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain the purpose of the 'text' or 'label' parameters. An agent cannot infer what text to pass or how label affects the call. The description adds no meaning beyond the generic parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Evaluate') and resource ('MCP runtime block/redact policy') and indicates the output type ('raw-free report-only interceptor result'). It distinguishes from siblings like observe_mcp_event (observation) and scan_mcp_config (scanning), though 'raw-free' and 'interceptor' are jargon that could confuse agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus its siblings. It does not mention alternatives, exclusions, or prerequisites. The only implicit signal is the verb 'Evaluate' suggesting policy enforcement, but nothing clarifies a selection condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_ruleBRead-onlyIdempotent
Explain a K-Guard rule in plain language with impact, fix steps, and verification.
| Name | Required | Description | Default |
|---|---|---|---|
| rule_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds that the tool provides impact, fix steps, and verification, which is useful context beyond a generic 'explain'. However, it does not disclose output format, error handling, or any other behavioral traits, so the added value is moderate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that front-loads the action and lists the key outputs. There is no fluff, and it is concise without being under-specified—every word contributes to describing the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and annotations covering safety, the description is minimal but conveys the purpose. However, it lacks usage guidance and parameter semantics, and it does not contextually place itself among the many sibling tools. An agent would not know when to call it or how to find the rule_id, leaving notable gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only the parameter name 'rule_id' with type string and no description. The tool description does not explain what a rule_id is, how to obtain it, or its format. With 0% schema description coverage, the description wholly fails to compensate, leaving the agent with no semantic context for the sole required parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Explain a K-Guard rule') on a specific resource, and lists the content of the explanation (impact, fix steps, verification). This distinguishes it from siblings like suggest_fix, which focuses on proposing fixes rather than explaining a rule.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives like suggest_fix or scan_workspace. It does not mention why one would prefer an explanation over a fix suggestion, nor any conditions or prerequisites, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
field_benchmarkC
Aggregate field benchmark reports by cohort; probes require explicit MCP probe opt-in.
| Name | Required | Description | Default |
|---|---|---|---|
| run_probes | No | ||
| review_path | No | ||
| manifest_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must convey behavioral traits such as side effects, return values, or error conditions. It only discloses that probes need explicit opt-in and that results are aggregated by cohort. It does not describe what the tool returns, whether it is read-only, or any side effects. The sparse disclosure leaves the agent uncertain about the tool's full behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no redundant wording. The primary action is stated first, followed by a specific constraint. Every word adds some value, and the structure is front-loaded with the main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three parameters, no output schema, and no annotations, the description is insufficient. It does not explain what manifest_path should reference, what run_probes does beyond the opt-in hint, or the role of review_path. An agent would need external knowledge to invoke this correctly. The description covers only a fraction of the necessary context for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must explain the parameters. It does not define manifest_path, run_probes, or review_path beyond their names. The mention of cohort and probe opt-in hints at some parameter usage, but not enough for an agent to correctly fill in all parameters without further knowledge. The description compensates poorly for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Aggregate') and resource ('field benchmark reports') with a clear qualifier ('by cohort'). It distinguishes the tool from siblings like create_benchmark_template or field_campaign_status, which deal with creation or status rather than aggregation. The probe opt-in clause adds a specific characteristic, but the description does not explicitly name any sibling for comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool over alternatives. It states a requirement about probes ('probes require explicit MCP probe opt-in') but does not explain when aggregation is appropriate or when to choose another tool. There is no mention of context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
field_campaign_statusC
Validate the app roster and emit a raw-free fail-closed campaign status.
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | Yes | ||
| roster_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. 'Fail-closed' hints at error handling but doesn't explain side effects, required permissions, or what happens on failure. 'Raw-free' suggests output transformation but is undefined.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no waste, but it sacrifices substance for brevity. It is concise in length but not in delivering necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With two required parameters and no output schema, an agent cannot infer what output looks like, what 'raw-free' means, or what 'fail-closed' implies. The description is inadequate for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must define both required parameters. It never mentions roster_path or output_path, leaving their purpose entirely unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific action ('validate') and resource ('app roster'), with a defined outcome ('emit a campaign status'). It is distinct from sibling validation tools by focusing on campaign status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives like validate_policy_controls or scan_workspace. It does not state prerequisites, exclusions, or selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
guardian_auditC
Run Guardian; korean_senior rows require four review domains and fail closed when fail_on is set.
| Name | Required | Description | Default |
|---|---|---|---|
| fail_on | No | ||
| run_sca | No | ||
| run_probes | No | ||
| manifest_path | Yes | ||
| previous_report_path | No | ||
| mcp_http_proxy_report_path | No | ||
| field_validation_report_path | No | ||
| control_validation_report_path | No | ||
| language_validation_report_path | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description must carry the full burden of behavioral disclosure. It does reveal two behaviors: the requirement for four review domains for korean_senior rows and the fail-closed behavior when fail_on is set. However, it does not disclose whether the tool is read-only, what it modifies, any authentication requirements, or any side effects. This is insufficient for an audit tool of this complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that combines two unrelated pieces of information with a semicolon. It is concise but severely under-specified for a tool with 9 parameters. The structure does not front-load the most important information about the tool's purpose or usage; instead it focuses on an edge case about korean_senior rows.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 9 parameters, no annotations, and no output schema, the description is drastically incomplete. An agent has no understanding of what the tool returns, what most parameters do, or what the audit covers. The description only addresses a narrow scenario, leaving the agent to guess about the rest.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate by explaining key parameters. It only hints at 'fail_on' by mentioning it in the fail-closed clause, but does not explain its values or purpose. Parameters like run_sca, run_probes, and various report paths are left completely unexplained, making it hard for an agent to know how to set them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Run Guardian' which identifies the action, but it does not explain what Guardian does or why it would be used. The mention of 'korean_senior rows' and 'four review domains' suggests a specific audit context, but without knowing what Guardian does, the purpose remains ambiguous. It distinguishes from siblings only by name, not by functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a specific condition: 'korean_senior rows require four review domains' and 'fail closed when fail_on is set.' This hints at when this tool might be needed, but it does not explicitly state when to use this tool over alternatives like deep_analyzer_audit or software_composition_audit. There is no guidance on exclusions or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
observe_mcp_eventC
Observe one MCP runtime event and return stream-ready block/redact policy decisions.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| label | No | mcp-runtime-event | |
| session_id | No | default |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'observe' which hints at read-only, but does not state whether the tool has side effects, modifies state, requires specific permissions, or what 'stream-ready block/redact policy decisions' entail. The description is too vague to inform an agent about side effects or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core action and output. It is concise with zero wasted words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, no output schema, and no annotations, the description is incomplete. It does not explain how to construct the input, what the return value looks like, or any operational context (e.g., streaming behavior, policy format). An agent calling this tool would lack essential details to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no explanation for parameters. The description does not compensate by explaining the role of 'text', 'label', or 'session_id'. There is no guidance on what content to pass or how these parameters influence the output. This is a significant gap for a 3-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('observe'), a specific object ('one MCP runtime event'), and the result ('return stream-ready block/redact policy decisions'). It clearly differentiates from sibling tools like observe_mcp_events (plural) and enforce_mcp_events by emphasizing the singular scope and the decision-returning action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus its siblings. The description implies usage for a single event but does not state when to prefer it over observe_mcp_events or enforce_mcp_events. No exclusions or alternative conditions are provided, leaving the agent to infer the context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
observe_mcp_eventsB
Observe MCP runtime/proxy JSONL events for PII, hidden instructions, and agentic/external flow.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| label | No | mcp-runtime-events |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does explain that the tool scans for PII, hidden instructions, and agentic/external flow, which gives insight into its function. However, it does not explicitly state whether the tool is read-only, has side effects, or requires network access. The term 'observe' implies passivity, but this is not confirmed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that delivers the core purpose upfront and appends the specific detection targets. It contains no superfluous words and is highly efficient, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description should at least hint at what the tool returns. It does not mention output format or return values. Additionally, it fails to explain the role of the optional 'label' parameter. For a tool with only two parameters, the description is reasonably complete for the primary action but leaves critical context gaps for a fully informed call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate for parameter clarity. The description implies that the 'text' parameter contains JSONL events (from 'Observe MCP runtime/proxy JSONL events'), but it does not explicitly map description content to parameters, and the 'label' parameter is completely unexplained. This leaves the agent uncertain about parameter formatting and purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (observe) and the resource (MCP runtime/proxy JSONL events) and specifies the focus (PII, hidden instructions, agentic/external flow). This distinguishes it from sibling tools like 'observe_mcp_event' (singular) and 'enforce_mcp_events' (different action) without needing to inspect their schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives. It does not mention specific scenarios or indicate conditions where a different tool would be more appropriate. No exclusions or alternative references are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
probe_httpC
Run safe read-only HTTP probes against localhost or explicitly authorized external targets.
| Name | Required | Description | Default |
|---|---|---|---|
| base_url | Yes | ||
| deep_active | No | ||
| session_file | No | ||
| authorization_note | No | ||
| external_authorized | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does state 'safe read-only', indicating non-mutating behavior, and restricts targets to localhost or authorized external, which is useful. However, it fails to disclose behaviors related to parameters, response format, authentication needs, or rate limiting, leaving significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose and target constraint. It avoids redundancy and is easy to parse. However, it is perhaps too brief given the other gaps, but for conciseness alone it is strong.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters, no output schema, and no annotations, a complete description would need to cover parameter semantics, expected return values, and use cases. This description only provides a high-level purpose and target restriction, leaving the agent without enough information to call the tool correctly. It is substantially incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining parameters. It offers no explanation for any of the five parameters (base_url, deep_active, session_file, authorization_note, external_authorized). The agent cannot infer their meaning or usage from the description, which is a severe deficiency.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a read-only HTTP prober with specific target constraints (localhost or explicitly authorized external targets). It uses a concrete verb ('Run') and a resource ('HTTP probes'), making the purpose unambiguous. While it doesn't name a sibling, no sibling tool appears similar, so it stands alone effectively.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for probing HTTP endpoints but provides no explicit scenarios, alternatives, or exclusions. It does not mention when to prefer another tool or when this tool should not be used. The guidance is inferred from the purpose statement rather than directly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_diffB
Scan only a git diff, without echoing raw diff content in the MCP response.
| Name | Required | Description | Default |
|---|---|---|---|
| base_ref | No | HEAD~1 | |
| head_ref | No | HEAD | |
| workspace_path | No | . |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states that raw diff content is not echoed, which is a meaningful behavior. However, it does not describe what the tool does return, whether it is read-only, or any side effects. For a scan tool, this is a partial disclosure; more detail on the output format would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that front-loads the core purpose and the key behavioral absence. It is concise with no redundant wording. However, given the tool's parameters and lack of annotations, it may be slightly under-specified, but it earns its place as an efficient opening statement.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has three parameters, no output schema, and no annotations. The description does not explain parameter semantics, expected output, or usage context beyond the scope of a git diff. An agent would lack crucial details to invoke the tool correctly, making the description inadequate for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% (no parameter descriptions), so the description must compensate. It does not mention base_ref, head_ref, or workspace_path at all. The parameter names and defaults in the schema give some hints, but the description adds no additional meaning, leaving an agent unsure how to set the refs or workspace path correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (a git diff) and the action (scan), and adds a specific behavioral distinction ('without echoing raw diff content in the MCP response'). This unmistakably differentiates it from sibling scan tools like scan_workspace and scan_text, which target different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (for scanning a git diff) but does not explicitly mention alternatives or exclusions. An agent can infer the primary use case, but there is no guidance on when NOT to use it versus scan_workspace or scan_text. The scope is clear enough to make an inference, so it is not a complete miss.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_mcp_configC
Discover and scan project MCP configs plus agent instruction and skill components.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | . |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Discover and scan' implies a read operation, but it doesn't state whether it modifies anything, what it returns, how thorough the scan is, or whether it only scans the given path. There is no disclosure of potential side effects or performance implications. Minimal value beyond the literal meaning.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise and front-loads the main verb. However, it is under-specified rather than efficiently detailed. Every word is used, but the ambiguity (e.g., 'discover and scan' vs just 'scan') means the sentence doesn't earn its place as the sole content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations and output schema, the description must explain the tool's return value and behavior. It does not mention what the scan produces (list of files? config contents?), how to interpret results, or whether the path is required (though default '.' is in schema). For a tool that likely serves as an inventory/analysis entry point, the description is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter 'path' with no description and 0% schema coverage. The description does not explain what 'path' refers to (directory root? file?) or how it affects the scan. Since the schema provides no semantic help and the description adds nothing, parameter understanding is left to guessing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb+resource pattern ('Discover and scan project MCP configs plus agent instruction and skill components'), which states what the tool does. It is specific enough about the resource type to distinguish from generic scan tools like scan_workspace, but it doesn't fully delimit scope (what counts as 'agent instruction and skill components' is vague). Not a tautology, but could be sharper.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no mention of when to use this tool versus the many sibling scanning tools (scan_workspace, scan_text, scan_diff, etc.). No context about typical use cases, prerequisites, or exclusions. The description offers no guidance on selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_textB
Scan an inline text snippet without writing it to disk.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| label | No | inline-text |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose one meaningful trait: the operation does not write to disk. But it doesn't mention whether the scan is read-only, what side effects or requirements exist, or what the return format is. This is partial disclosure, but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that leads with the primary action and key constraint. Every word adds value, and it avoids redundant phrasing. It is appropriately sized for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, no output schema), the description covers the core behavior but leaves gaps: parameter semantics are unexplained, and differentiation from sibling scan tools (scan_workspace, scan_diff, etc.) is only implicit. An agent might not know exactly when to choose this over alternatives without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no information about the parameters. While the names 'text' and 'label' are fairly self-explanatory, the description does not clarify expected formats, defaults, or how the label is used. The description fails to compensate for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the verb 'scan' and the resource 'inline text snippet', clearly distinguishing it from file- or workspace-based scans. However, it doesn't specify the type or goal of the scan (e.g., security, content analysis), leaving some ambiguity about what the scan actually does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'inline text snippet' and 'without writing it to disk' strongly imply the intended use case: scanning text passed directly rather than from a file. However, it doesn't explicitly name alternative tools (e.g., scan_workspace, scan_diff) or state when those would be preferred, so the usage guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_workspaceB
Scan a local workspace for secrets, Korean PII, composite PII, config risks, and flow risks.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| include_flow | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does not state whether the scan is read-only, whether it modifies files, what 'flow risks' entail, or what the return format looks like. The tool appears to be a scan, but its side effects and operational implications are opaque, which is a significant gap for a security-related tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no wasted words. It leads with the action and resource, then lists the detection categories. It is efficient, though it could be improved by adding a sentence about parameters or usage without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (scanning a whole workspace for multiple risk types) and the absence of an output schema and annotations, the description is insufficient. It does not explain what results are returned, how to interpret them, or the role of the include_flow parameter, leaving an agent with significant uncertainty about how to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must explain each parameter, but it only indirectly references 'path' via 'local workspace' and completely omits the 'include_flow' boolean. An agent would not know what toggling 'include_flow' does or why it defaults to true. The description fails to compensate for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Scan'), a resource ('local workspace'), and enumerates the exact categories it detects (secrets, Korean PII, composite PII, config risks, flow risks). This clearly distinguishes it from sibling tools like scan_text or scan_diff that operate on different input types, so an agent can immediately tell what this tool is for.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (scanning a directory) via the phrase 'local workspace' and the required 'path' parameter, but it does not explicitly state when to choose this over alternatives like scan_text or scan_diff, nor does it mention any prerequisite conditions. The guidance is present but only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
score_fixture_corpusB
Evaluate a local fixture corpus and return FP/FN precision/recall scoreboard.
| Name | Required | Description | Default |
|---|---|---|---|
| corpus_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions returning a scoreboard but does not disclose whether the operation is read-only, whether it modifies the corpus, requires specific permissions, or has side effects. The lack of explicit safety cues is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence front-loads the core purpose and output. Every word is informative with no filler, earning a top score for efficiency and clarity of structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one required param, no output schema), but the description lacks essential context: required input format, what constitutes a corpus, and any prerequisites. With no annotations to fall back on, this is not complete enough for confident invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain the 'corpus_path' parameter. It only hints at 'local fixture corpus' without specifying path format, file/directory requirements, or any constraints. This is insufficient for an agent to determine correct input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Evaluate'), a clear resource ('local fixture corpus'), and an explicit output ('FP/FN precision/recall scoreboard'). It uniquely identifies this tool among siblings, which are focused on scanning, validation, and audits rather than scoring a fixture corpus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives or any exclusions. The description only states what it does, leaving the agent to infer applicability without any contextual cues or usage conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
security_gateCRead-onlyIdempotent
Run the quick workspace gate. Use guardian_audit(fail_on=...) for the canonical release gate.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | . | |
| fail_on | No | high | |
| include_flow | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering mutation safety and side effects. The description adds 'quick' and 'workspace' context, which is slightly beyond annotations. However, it does not disclose what the gate actually does (e.g., what it scans, how it reports failures, whether it touches files). With annotations present, a 3 is appropriate – it adds minimal context but does not contradict or amplify the safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short (two sentences) and front-loaded with the action, but it is under-specified. There is no wasted wording, yet the conciseness does not earn its place because it conveys almost no actionable information. A description this brief would be acceptable only if the schema and annotations carried the load, but they do not for parameters and purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no parameter descriptions, and a minimal description. There is no explanation of what the gate checks, what fail_on levels mean, what include_flow does, or how results are returned. Even with annotations, the agent lacks essential context to decide when and how to call this tool correctly. The description is far from complete for a security gate with three parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description does not mention any of the three parameters (path, fail_on, include_flow). The agent must rely purely on parameter names and defaults, which is insufficient for correct invocation. The description completely fails to compensate for the lack of schema descriptions, leaving parameter semantics entirely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Run the quick workspace gate' – a verb and resource, but 'workspace gate' is vague and does not specify what checks are performed, what outcomes are produced, or what 'gate' means in this context. It distinguishes from guardian_audit but does not define its own purpose clearly. A more specific statement like 'Run a lightweight security scan on the workspace' would be clearer.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly names guardian_audit as the canonical release gate, implying this tool is for a quicker, non-canonical check. This is partial guidance – it tells the agent when *not* to use this tool (for canonical releases) but does not state conditions for when to use this tool itself. It lacks direct 'use this when...' language, but the alternative reference provides some routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
software_composition_auditB
Audit Python/npm/Go locks; explicit authorization is required because engines may consult advisory databases.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace_path | Yes | ||
| authorize_advisory_lookup | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait: explicit authorization is required because engines may consult advisory databases, implying external network access. However, it does not state whether the tool modifies files or only reads, nor does it describe any other side effects. The disclosure is useful but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core purpose and then adds the critical authorization note. There is no fluff or redundancy; every word contributes to understanding the tool's function and a key prerequisite.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is incomplete for a tool that audits lock files. It does not explain what 'audit' means in terms of output (e.g., vulnerability report, risk score), what happens during the audit, or how parameters like 'authorize_advisory_lookup' affect behavior. With no output schema and only minimal description, an agent would struggle to correctly invoke and interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It barely touches on the 'authorize_advisory_lookup' parameter via the authorization mention, but it does not explain either parameter's purpose or how to set them. The description adds minimal value beyond what the schema shows (names and defaults). This is insufficient for a tool where parameters are not self-explanatory.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool audits Python/npm/Go locks, specifying the verb 'audit' and the target resources. It distinguishes from generic audit tools like deep_analyzer_audit by focusing on lock files for specific ecosystems. However, it does not explicitly name sibling alternatives, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: use when you need to audit lock files for Python, npm, or Go. It does not provide explicit when-not guidance or mention alternatives, but the scope is clear enough to infer the primary use case. No exclusions or alternative suggestions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_review_before_shipD
Start Guardian high only after a completed first review of the same source snapshot.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | 이 릴리스에서 변하지 않는 앱 식별자(예: my-shop-web) | |
| live_url | No | 선택 사항인 실제 앱 URL. 승인 없는 외부 능동 검사는 실행하지 않습니다. | |
| user_scope | Yes | 사용자와 권한 범위(예: anonymous, member, admin) | |
| data_classes | Yes | 다루는 데이터 종류. 쉼표로 구분합니다(예: account, korean_pii, payment) | |
| workspace_path | No | 설치 시 고정된 프로젝트는 '.'을 사용합니다. | . |
| live_authorized | No | 외부 live 대상의 능동 검수 권한을 운영자가 확인했는지 여부 | |
| run_live_review | No | 허가된 live URL에 동적 검수를 실행할지 여부 | |
| scope_proof_ref | Yes | 검수 권한을 확인할 수 있는 운영자 측 근거의 참조값. 비밀 원문은 넣지 않습니다. | |
| business_purpose | Yes | 앱이 실제로 제공하는 기능과 이용 목적 | |
| public_endpoints | No | 허가된 공개 엔드포인트 목록(예: /,/api/health) | |
| initial_review_id | Yes | 완료된 check_my_app 검수 번호. 코드 수정 후에는 check_my_app을 다시 실행한 번호여야 합니다. | |
| authorization_note | No | 능동 검수 권한 범위에 대한 짧은 설명. 자격증명 원문은 넣지 않습니다. | |
| previous_report_path | No | 선택 사항인 이전 raw-free Guardian 보고서 경로 | |
| run_software_composition | No | 의존성·라이선스·공급망 분석을 함께 실행합니다. |
Output Schema
| Name | Required | Description |
|---|---|---|
| method | Yes | |
| findings | No | |
| experience | Yes | |
| review_job | No | |
| guardian_gate | No | |
| review_receipt | No | |
| primary_workflow | No | |
| application_assurance | No | |
| auditor_qualification | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not reveal what side effects occur, whether the action is destructive, what permissions are needed, or what operational impact 'starting Guardian high' has. The tool appears to be a state-changing action with zero transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but grossly under-specified. It omits essential information and does not front-load any useful detail. Conciseness without substance is not effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (14 parameters, 6 required) and an output schema, the description needs to explain the tool's purpose, return value, and usage context. It does none of that. The single sentence leaves an agent without enough information to know when or how to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 14 parameters. The description adds no parameter-specific meaning, which aligns with the baseline of 3 when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description says 'Start Guardian high' but never defines what 'Guardian high' is. It mentions a precondition (after a completed first review) but does not state the tool's actual function, resource, or outcome. This is vague and could be confused with sibling tools like continue_review or guardian_audit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides one usage condition (only after a completed first review) but gives no guidance on when to use this tool versus siblings like check_my_app, continue_review, or guardian_audit. There is no mention of alternatives or exclusions beyond the single prerequisite.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
suggest_fixCRead-onlyIdempotent
Return a dry-run fix recipe and regression-test suggestions for a rule.
| Name | Required | Description | Default |
|---|---|---|---|
| rule_id | Yes | ||
| framework | No | generic |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and idempotentHint=true, so the description correctly aligns by stating 'dry-run,' which implies no changes are applied. However, it does not mention any additional behavioral aspects like rate limits, required authentication, or the shape of the response. It adds a small amount of context about being a dry-run but stays within the safety profile the annotations already establish.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, focused sentence that states the core functionality without any filler. The key concept (dry-run fix recipe) is front-loaded, and there is zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's relative simplicity (two parameters, read-only operation) and the presence of safety annotations, the description covers the basic operation. However, without an output schema, the agent does not know what a 'fix recipe' or 'regression-test suggestions' looks like, and the framework parameter's effect is unaddressed. This makes the description incomplete for a correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no descriptions for rule_id or framework, and the description only refers to 'a rule,' which indirectly hints at rule_id but does not explain what a rule is or how framework modifies the output. With 0% schema coverage and no parameter explanation, the description fails to compensate, leaving the agent to guess at the meaning and semantics of both parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a clear action (return) and a specific object of that action: a dry-run fix recipe and regression-test suggestions for a rule. It distinguishes itself from siblings like explain_rule by focusing on fixes rather than explanations, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no explicit guidance on when to choose this tool over the many siblings, such as explain_rule or scan tools. The only implicit usage is that it applies to a rule, but there is no information about prerequisites, contexts, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_multilang_packC
Run the pinned nine-language pack twice; this does not claim field accuracy.
| Name | Required | Description | Default |
|---|---|---|---|
| pack_path | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description is the sole source of behavioral disclosure. It discloses that the tool runs twice and explicitly states it does not claim field accuracy, which is a useful limitation. However, it does not describe side effects, return values, or what the validation process actually does beyond running twice, leaving significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the action and includes a caveat. It contains no redundant words or unnecessary detail, making it concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and no output schema, the description should explain what pack_path should point to, what running twice accomplishes, and what the expected output or result indicates. The description only instructs to run it twice and disclaims accuracy, which is insufficient for an agent to invoke it correctly without guessing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter (pack_path) with 0% description coverage, meaning the schema provides no meaning. The tool description does not mention the parameter at all, offering no context about what path to supply, its format, or its purpose. The description completely fails to compensate for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Run the pinned nine-language pack twice') on a named resource, making the primary action clear. The tool name 'validate_multilang_pack' reinforces the purpose of validation, and the caveat 'does not claim field accuracy' differentiates it from accuracy-focused tools. However, it does not explicitly state that it validates a multilingual pack, relying on the name for that.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus siblings like validate_policy_controls or scan_workspace. It does not mention prerequisites, scenarios, exclusions, or alternatives. An agent has no basis for choosing this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_policy_controlsC
Run the repeated synthetic JIT/JEA and database AST/RBAC/isolation control pack.
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'Run', implying execution but does not state side effects, whether it is read-only, what output it produces, or any dependencies. The phrase 'repeated synthetic' hints at repetition but remains vague and unexplained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no redundant words. It front-loads the primary action and resource. While it lacks structure or headers, it is efficient and easy to read for its length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool lacks an output schema, annotations, and rich parameter documentation. The description gives only a terse summary that assumes domain knowledge about JIT/JEA and AST/RBAC. It does not explain what the validation entails, what the output looks like, or any prerequisites, leaving the agent under-informed for a validation/control tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description does not mention the sole parameter 'output_path' at all. The parameter name suggests it is an output destination, but that is not clarified. The description adds no meaning beyond what the schema already shows, and fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Run') and a specific resource ('the repeated synthetic JIT/JEA and database AST/RBAC/isolation control pack'). It provides enough specificity to distinguish it from siblings like validate_multilang_pack, though the jargon-heavy phrasing ('synthetic JIT/JEA' and 'AST/RBAC/isolation') may be ambiguous without domain context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention any sibling tools, conditions, or exclusions. The description simply states what it does, leaving the agent to infer context from the name and context signals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_streamable_http_runtimeD
Run the repeated Streamable HTTP authorization, lifecycle, SSE, and audit matrix.
| Name | Required | Description | Default |
|---|---|---|---|
| output_path | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavior disclosure. It only says it 'runs' something, without disclosing side effects, read-only vs mutating nature, auth requirements, or output format. This is a serious gap for a tool that likely performs complex validation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, but it is under-specified rather than concise. It contains almost no actionable information, so the words do not earn their place. It is more of a heading than an explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that likely executes a multi-faceted validation matrix, the description provides no context on what the matrix entails, how to interpret results, or how the output_path is used. With no output schema and minimal description, an agent cannot confidently call this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, output_path, has 0% schema description coverage and the description does not mention it at all. The description adds no meaning to the parameter, failing to compensate for the schema's lack of explanation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Run') and resource ('Streamable HTTP'), and lists topics it covers (authorization, lifecycle, SSE, audit). However, 'repeated ... matrix' is vague and does not clearly convey what the tool actually does. It does not explicitly differentiate from sibling validation tools like validate_policy_controls or validate_multilang_pack, but the resource name gives some distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus its siblings. It does not mention prerequisites, scenarios, or exclusions. An agent would have no cue for choosing this over other validation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
28 tool updates
v0.1.0- First observed
build_flow_map - First observed
check_my_app - First observed
continue_review - First observed
create_benchmark_template - First observed
create_field_campaign_template - First observed
create_guardian_manifest_template - First observed
data_release_gate - First observed
deep_analyzer_audit - First observed
enforce_mcp_events - First observed
explain_rule - First observed
field_benchmark - First observed
field_campaign_status - First observed
guardian_audit - First observed
observe_mcp_event - First observed
observe_mcp_events - First observed
probe_http - First observed
scan_diff - First observed
scan_mcp_config - First observed
scan_text - First observed
scan_workspace - First observed
score_fixture_corpus - First observed
security_gate - First observed
software_composition_audit - First observed
start_review_before_ship - First observed
suggest_fix - First observed
validate_multilang_pack - First observed
validate_policy_controls - First observed
validate_streamable_http_runtime
TDQS
Scored across 28 tools
Most tools have clearly distinct purposes with detailed descriptions. However, a few near-duplicates exist (e.g., observe_mcp_event vs observe_mcp_events, security_gate vs guardian_audit) that could slightly confuse an agent. Overall, the set is well-separated.
Tool names mix conventions inconsistently. Many follow verb_noun (scan_workspace, create_field_campaign_template, explain_rule), but several use noun_noun (software_composition_audit, data_release_gate, field_campaign_status, security_gate). There is also singular/plural inconsistency (observe_mcp_event vs observe_mcp_events). This makes naming patterns unpredictable.
With 28 tools, this exceeds the 25+ threshold and feels heavy. While the domain is broad, many tools overlap in function (multiple validation and audit tools). The server could consolidate some related tools (e.g., separate scan_* tools) without losing clarity.
The tool surface covers the main security workflow: scanning, validation, audits, templates, explanations, fixes, and status gates. Minor gaps exist (e.g., no explicit rule management or history retrieval), but the core lifecycle is present and agents can complete typical tasks.
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