DevTwin MCP
DevTwin MCP
Дайте ИИ-агентам для написания кода живое, структурированное понимание вашего локального окружения разработки.
DevTwin — это сервер Model Context Protocol (MCP), который отвечает на один центральный вопрос для ИИ-агента, пишущего код: почему окружение этого разработчика отличается, сломано или нездорово?
Он определяет технологию проекта, проверяет установленные версии рантайма на соответствие тому, что проект реально требует, инспектирует состояние зависимостей и lockfile-файлов, находит требуемые локальные сервисы (Postgres, Redis, ...) и проверяет, запущены ли они, проверяет порты и состояние Git, и превращает всё это в структурированную, основанную на фактах диагностику — без отправки вашего окружения в облачный бэкенд и без раскрытия секретных значений модели.
Содержание
Related MCP server: devscope
Зачем существует DevTwin
ИИ-агенты, пишущие код, хорошо читают код, но слепы к окружению, в котором этот код реально работает.
«Почему
npm testпадает на моей машине?» обычно не имеет отношения к коду — это несовпадение версии Node, не запущенный сервис или зависимости, которые никогда не были установлены.DevTwin даёт агенту тот же сигнал, который старший инженер собрал бы вручную —
node --version,git status,lsof -i :5432,docker ps— в виде структурированных вызовов инструментов, а не догадок.
FAQ: у Claude CLI уже есть shell, так что зачем вообще MCP?
Обычно это первый вопрос, который задаёт разработчик, и он справедлив.
В клиенте вроде Claude Code, где уже есть инструмент Bash, можно просто
попросить его выполнить node --version, docker ps, lsof -i :5432 и т.д.
напрямую — никакой MCP-сервер не нужен. Пробел, который закрывает DevTwin, —
не «можно ли это вообще сделать» — а вот что:
Без DevTwin (сырой Bash) | С DevTwin |
Агент может выполнить что угодно, включая разрушительные команды, даже случайно. | Ноль произвольного выполнения — только фиксированный список разрешённых read-only/безопасных проверок. См. Модель безопасности. |
Каждый раз выбирает другое расследование; может упустить крайние случаи экосистем (Gradle wrapper vs. системный Gradle, | Одна и та же курируемая, протестированная проверка каждый раз, для каждой экосистемы. |
Команда вроде | Структурно никогда не возвращает секретные значения — только наличие/отсутствие. См. Модель конфиденциальности. |
Работает только в клиентах, где вообще есть инструмент shell (не в Claude Desktop, в некоторых плагинах IDE). | Работает в любом MCP-клиенте, с shell или без. |
~6 отдельных round-trip'ов, чтобы диагностировать один сбой. | 1 вызов. См. разобранный пример. |
Честный ответ конкретно для Claude CLI: поскольку там уже есть Bash, выигрыш DevTwin здесь меньше, чем «возможность, которой у вас не было» — это гарантии безопасности и стабильный структурированный вывод, а не принципиально новый доступ. Именно поэтому он и не бесплатен — см. Стоимость в токенах за то, что подключение реально стоит, и когда оно оправдано.
Ещё несколько вопросов, которые стоит задать перед внедрением:
«Разве это не просто скрипт doctor (make doctor, bin/setup) с лишними
шагами?» Концептуально да — во многих зрелых репозиториях уже есть такой
самописный скрипт. Отличие DevTwin в том, что в большинстве репозиториев его
нет, написать хороший скрипт для каждой экосистемы — это реальная работа, его
вывод — это структурированный JSON, с которым агент может рассуждать, а не
простой текст для чтения человеком, и одни и те же 10 инструментов работают
одинаково в любом репозитории, вместо самодельного скрипта на каждый проект со
своими соглашениями и слепыми зонами.
«Это работает только с Claude / Claude Code?» Нет. DevTwin говорит на стандартном протоколе Model Context Protocol — любой MCP-совместимый клиент (Claude Desktop, Cursor, Windsurf и т.д.) может подключиться к нему так же. Ничего в нём не специфично для Claude.
«Безопасно ли на него полагаться — активно ли он поддерживается?» Это статус Alpha и молодой проект — прочитайте код (он короткий), прежде чем доверять ему рабочий процесс, от которого вы зависите, как и любой новой зависимости в инструментах разработки.
«Может ли он предложить что-то неправильное или автоматически выполнить
плохую рекомендацию?» Ни один инструмент здесь не выполняет строку
recommendations — это просто текст для агента (или вас), чтобы прочитать и
решить. dev_check — единственный инструмент, который что-то выполняет, и
только те команды, которые он сам распознал из файлов проекта, проверенные по
фиксированному списку разрешённых, с shell=False и таймаутом — см.
Модель безопасности.
«Отправляет ли он данные домой или телеметрию куда-либо?» Нет. Ноль собственных сетевых вызовов — см. Локальная архитектура.
«Я не хочу, чтобы MCP-сервер выполнял любые команды на моей машине.»
9 из 10 инструментов — чисто read-only (чтение файлов, проверки версий).
Только dev_check что-то выполняет, и только те команды, которые DevTwin сам
распознал из файлов проекта, проверенные по списку разрешённых, с
shell=False и таймаутом — см. Модель безопасности за
точное описание того, что это разрешает, а что нет.
Преимущества
Меньше неверных диагнозов. Без DevTwin агент, отлаживающий сбой, может только читать код и догадываться — он часто предложит исправление кода для того, что на самом деле является несовпадением версии Node или остановленной базой данных. DevTwin даёт ему факты вместо догадок.
Один вызов вместо многих. Один вызов
dev_healthобъединяет ~10 базовых проверок (версии рантайма, состояние зависимостей, сервисы, порты, Git) в один структурированный результат с оценкой — вместо того, чтобы агент делал дюжину отдельных shell-запросов и каждый раз разбирал сырой вывод CLI.Одна и та же проверка каждый раз. Точные проверки для каждой экосистемы (Gradle wrapper vs. системный Gradle,
.nvmrcvs.package.jsonengines, ...) закодированы один раз, поэтому диагноз стабилен между сессиями, а не зависит от того, что агенту пришло в голову выполнить.Безопаснее, чем дать агенту shell. Никакого произвольного выполнения команд, никаких разрушительных операций, никогда — см. Модель безопасности.
Секреты не трогаются. Переменные окружения, которые выглядят как секретные, проверяются только на наличие; значения никогда не читаются и не возвращаются — см. Модель конфиденциальности.
Работает даже там, где у агента нет shell. MCP-клиенты без инструмента Bash (некоторые IDE-ассистенты, ограниченные агенты) получают эту возможность вообще, а не ноль возможностей.
Стоимость в токенах
Реальные цифры, а не оценка — измерено напрямую из схем инструментов этого
сервера (mcp.list_tools()) и реального ответа dev_health(), с использованием
стандартного приближения ~4 символа на токен.
Два разных момента тратят токены, и стоят они очень по-разному:
Когда | Что происходит | Стоимость |
В момент подключения клиента к DevTwin | Все 10 схем инструментов (имя, описание, параметры) добавляются в каждый запрос в этой сессии — независимо от того, вызывается ли какой-либо инструмент. Это верно для любого MCP-сервера, не только для DevTwin. | ≈1 400 токенов, каждый ход |
Только когда инструмент реально вызван | JSON-ответ этого одного инструмента добавляется в контекст, один раз. | ~120–200 токенов за вызов (зависит от того, сколько проблем найдено) |
Разбивка схем по инструментам (измерено):
Инструмент | Размер схемы | ≈ токенов |
| 440 символов | ~110 |
| 500 символов | ~125 |
| 470 символов | ~117 |
| 793 символа | ~198 |
| 523 символа | ~130 |
| 507 символов | ~126 |
| 507 символов | ~126 |
| 771 символ | ~192 |
| 645 символов | ~161 |
| 481 символ | ~120 |
Итого (все 10 инструментов) | 5 637 символов | ≈1 400 |
Честный итог: для одноразовой диагностики в сессии, которая в остальном никогда не касается вопросов окружения, raw Bash может оказаться дешевле по суммарным токенам — фиксированный налог в ~1 400 токенов на схемы часто перевешивает экономию от замены нескольких shell-команд одним вызовом. См. сравнение ниже с реальными цифрами по обеим сторонам.
Аргумент в пользу DevTwin становится сильнее, чем больше вопросов об окружении возникает в одной сессии (фиксированный налог платится один раз; каждый следующий вопрос — это ~150 токенов на DevTwin против сотен на raw Bash каждый раз) — а его реальное преимущество не в сыром количестве токенов, а в стабильности, безопасности и работе в MCP-клиентах, где нет инструмента Bash. См. Преимущества и Честные компромиссы.
Практическое следствие: регистрируйте DevTwin по-проектно, а не для всех пользователей, чтобы фиксированный налог платился только в сессиях, где он реально полезен — см. Использование на другом проекте.
Честные компромиссы
DevTwin — не инструмент ежедневного использования для стабильного окружения: никому не нужно перепроверять «запущен ли Postgres» на каждой функции, которую он пишет. Это инструмент для экстренных случаев: высокая ценность в конкретные моменты (свежий клон, загадочно падающая сборка, прямо перед коммитом), и простаивает в остальное время. Это предполагаемый паттерн использования, а не недостаток.
Налог на токены платится на каждом ходу с момента подключения, используется он или нет — см. Стоимость токенов с реальными замерами.
Он не всегда выигрывает по токенам для одного разового вопроса; он выигрывает в консистентности, безопасности и доступе к клиентам без оболочки — см. Преимущества.
Если у агента уже есть полный доступ к оболочке в репозитории, который вы полностью контролируете, и он редко сталкивается с расхождением окружения, DevTwin там может вообще не понадобиться.
DevTwin больше всего оправдывает себя на: общих/онбординговых репозиториях, менее доверенных или не имеющих оболочки настройках агентов и мультиэкосистемных монорепозиториях, где «что мне вообще проверять» само по себе является сложной задачей.
С DevTwin и без: наглядный пример
Скажем, вы спрашиваете агента: «почему npm test падает?» — а настоящая причина в несоответствии версии Node и незапущенном Postgres.
Без DevTwin (агент использует чистый Bash) — ему приходится угадывать правильную последовательность, по одной команде за раз:
cat package.json # spot "engines": {"node": ">=20"}
node --version # v16.20.0 -- mismatch found
grep -i "pg\|postgres" package.json # spot the Postgres dependency
cat .env # risk: may print a real secret into context
lsof -i :5432 # nothing listening
docker ps # check if it's in a container insteadШесть обращений туда-обратно, путь исследования, который агенту пришлось придумать, реальный шанс утечки секрета в разговор на шаге 4 и примерно 400–800 токенов текста команд и вывода (зависит от размеров файлов и количества запущенных Docker-контейнеров).
С DevTwin — один вызов:
dev_health(){
"status": "error",
"summary": "2 issues found: runtime drift, service down",
"issues": [
"Node 16.20.0 installed, project requires >=20 (from package.json engines)",
"Postgres required (found in docker-compose.yml) but not running on 5432"
],
"recommendations": [
"nvm install 20 && nvm use 20",
"docker compose up -d postgres"
]
}Тот же вывод, ~150 токенов за ответ — плюс фиксированные ~1400 токенов схемы, которые в любом случае уже оплачены в этом ходе (см. Стоимость токенов). Один вызов вместо шести, никакой возможности утечки секрета и каждый раз одна и та же выверенная проверка вместо импровизированного исследования, которое меняется от сессии к сессии.
Примеры вопросов, которые это открывает
«Проверь моё окружение разработки».
«Почему мой Kotlin-проект не собирается?»
«Соответствует ли моя версия Node этому репозиторию?»
«Почему моё приложение не может подключиться к Postgres?»
«Отличается ли моё окружение от того, что ожидает этот репозиторий?»
«Что мне запустить перед коммитом?»
«Я только что склонировал репозиторий — что мне нужно сделать, чтобы запустить его?»
Примеры по языкам
По одной строке на поддерживаемую экосистему: вопрос, который вы реально зададите, что DevTwin проверяет для ответа на него и тестовую/сборочную команду, которую он распознаёт для dev_check.
Экосистема | Пример вопроса | Что проверяется | Распознаваемые команды |
Python | «Подходит ли моя версия Python для этого репозитория?» |
|
|
Node.js | «Почему |
|
|
JVM (Java + Kotlin + Android) | «Почему моё Android-приложение не собирается после свежего клонирования?» | версия |
|
Go | «Соответствует ли моя версия Go этому репозиторию?» |
|
|
Rust | «Почему |
|
|
.NET | «Почему | наличие и версия SDK |
|
Swift (iOS/macOS) | «Почему моя iOS-сборка падает?» |
|
|
Ruby | «Почему |
|
|
PHP | «Почему моё PHP-приложение не запускается?` |
|
|
Универсальный (запасной) | «Этот репозиторий не на одном из языков выше — что вы можете мне сказать?» | сервисы |
|
Архитектура
Один MCP-сервер, много адаптеров экосистем — а не отдельный сервер на каждый язык.
MCP server -> core (workspace/detector/health/drift/diagnostics) ->
adapters (python/node/jvm/go/rust/dotnet/swift/ruby/php/generic) ->
system inspection (os/process/ports/env/fs/docker) ->
service detection (postgres/redis/generic)Подробности в docs/architecture.md. Как добавить новый языковой адаптер: docs/adapters.md.
Поддерживаемые экосистемы
Экосистема | Обнаруживается по | Проверяемая среда выполнения | Менеджеры пакетов |
Python |
|
| uv, pip, poetry, pipenv |
Node.js |
|
| npm, pnpm, yarn, bun |
JVM (Java + Kotlin) |
|
| Gradle (с учётом wrapper), Maven (с учётом wrapper) |
Go |
|
| go modules |
Rust |
|
| cargo |
.NET |
|
| NuGet |
Swift (iOS/macOS) |
|
| SPM, CocoaPods |
Ruby |
|
| Bundler |
PHP |
|
| Composer |
Универсальный (запасной) |
| -- | make/task/just/docker |
Любой проект, не подходящий ни под один конкретный адаптер, всё равно получит полезный вывод от универсального адаптера — DevTwin никогда не возвращает пустоту для нераспознанного проекта.
Установка
uv pip install devtwin-mcp
# or
pip install devtwin-mcpДля локальной разработки с клоном этого репозитория см. docs/development.md.
Конфигурация MCP-клиента
Точный синтаксис конфигурации зависит от клиента — обратитесь к документации вашего клиента. В общем случае DevTwin — это stdio MCP-сервер, который вызывается так:
{
"mcpServers": {
"devtwin": {
"command": "devtwin"
}
}
}Для локальной разработки из клона (без установки пакета):
{
"mcpServers": {
"devtwin": {
"command": "uv",
"args": ["run", "--directory", "/absolute/path/to/devtwin-mcp", "devtwin"]
}
}
}Проверьте обнаружение инструментов с помощью MCP Inspector:
npx @modelcontextprotocol/inspector uv run devtwinИспользование в другом проекте (для других разработчиков)
DevTwin — это один бинарник: подключайте сколько угодно проектов к одной установке, переустановка для каждого проекта не нужна. Две области действия:
Область действия | Загружается | Когда использовать |
Проектная (рекомендуемая по умолчанию) | Только в этом репозитории | Выбор по умолчанию — см. Стоимость токенов, почему |
Пользовательская | В каждом проекте, в каждой сессии | Когда вы обращаетесь к DevTwin в большинстве своих репозиториев |
Проектная область — положите .mcp.json в корень проекта:
{
"mcpServers": {
"devtwin": {
"command": "/absolute/path/to/devtwin-mcp/.venv/bin/devtwin"
}
}
}или с помощью Claude Code CLI:
claude mcp add devtwin /absolute/path/to/devtwin-mcp/.venv/bin/devtwin --scope projectПользовательская область:
claude mcp add devtwin /absolute/path/to/devtwin-mcp/.venv/bin/devtwin --scope userПосле добавления перезапустите клиент (или переподключите MCP-сервер), а затем просто задавайте обычные вопросы — см. Примеры вопросов, которые это открывает.
Совет по монорепозиториям: в репозитории со смешанными платформами (например, Android + iOS + бэкенд) адресуйте вопросы к конкретной подпапке, а не к корню репозитория — например, «проверь здоровье приложения android/». dev_detect в корне смешанного репозитория сообщает обо всех найденных экосистемах, что полезно один раз, но избыточно для точечной проверки.
Справочник инструментов
Все инструменты возвращают {status, summary, data, issues, recommendations}. status — одно из значений: ok, warning, error, unknown.
Tool | Класс | Описание |
| только чтение | Быстрое, файловое определение проекта/экосистемы с подтверждениями. |
| только чтение | Полная оценка здоровья 0–100, сочетающая состояние среды выполнения, зависимостей, сервисов и Git. |
| только чтение | Сравнивает требуемые и фактически установленные версии сред выполнения/инструментов. |
| только чтение | Диагностирует заданное сообщение об ошибке и выдаёт ранжированные корневые причины с подтверждениями. |
| только чтение | Подробная проверка проекта: среды выполнения, инструменты сборки, команды, ОС, Git. |
| только чтение | Состояние зависимостей/лок-файлов по каждой экосистеме. |
| только чтение | Требуемые локальные сервисы (Postgres, Redis, compose-сервисы) и их состояние выполнения. |
| безопасное выполнение | Выполняет распознанные тестовые/линтерные команды (например, |
| только планирование | Создаёт план подготовки для свежесклонированного репозитория; сам его никогда не выполняет. |
| только чтение | Сводка готовности к коммиту: состояние Git, здоровье, подготовленные файлы, похожие на секреты. |
Модель безопасности
Никакого произвольного выполнения команд. Инструмента
execute_shellне существует.dev_checkзапускает только те команды, которые DevTwin сам распознал в файлах проекта, проверяет их по разрешённому списку, запускает сshell=Falseи тайм-аутом.Никогда никаких разрушающих действий. DevTwin никогда не запускает
git reset --hard,rm -rf,kill -9,docker compose down, не удаляет лок-файлы и не изменяет.env.dev_prepareтолько планирует. Он классифицирует каждый предлагаемый шаг (read_only/safe/requires_approval/dangerous) и сам никогда ничего не выполняет.
Подробнее: docs/security.md.
Модель приватности
Переменные окружения проверяются только на наличие, когда их имя выглядит секретным (
PASSWORD,TOKEN,SECRET,API_KEY,PRIVATE_KEY,ACCESS_KEY,AUTH,CREDENTIAL, ...) — значения никогда не возвращаются.Файлы
.envсканируются только на имена переменных.dev_precommitпомечает подготовленные имена файлов, выглядящие как секретные, не читая и не сообщая их содержимое.
Локально-ориентированная архитектура
Нет серверного компонента, нет учётной записи, нет собственных сетевых вызовов, кроме локальных команд, которые он проверяет (
git,docker, языковые тулчейны).Всё, о чём он сообщает, берётся из файлов и процессов, уже находящихся на машине, на которой он работает.
Разработка
uv sync --all-extras
uv run pytest
uv run ruff check .
uv run mypy src
uv run devtwinПолный рабочий процесс см. в docs/development.md.
Участие
См. CONTRIBUTING.md. Добавление новой языковой экосистемы
— самый частый вид вклада: шаблон — в docs/adapters.md,
или src/devtwin/adapters/swift.py,
ruby.py и
php.py — как настоящие, принятые примеры для
создания своего адаптера.
Дорожная карта
Добавление адаптеров экосистем: Elixir, Dart, Scala, C/C++ (CMake/Bazel/Buck), Nix (о том, как добавить новый, см.
docs/adapters.md)Дополнительные детекторы сервисов (rip MySQL/MariaDB, MongoDB, Kafka, RabbitMQ)
Расширенное сравнение отклонений с конфигурацией CI (например, матрицы сред выполнения в GitHub Actions)
Опциональное локальное кеширование затратных проверок между вызовами инструментов в рамках сессии
Лицензия
Apache-2.0 — см. LICENSE.
Available Tools
10 toolsdev_checkA
Run recognized project checks (tests/lint) detected from project files,
e.g. pytest, ./gradlew test, npm test, cargo test. Only commands
DevTwin itself recognized are ever executed (never an arbitrary string),
each with a timeout. Pass run to restrict to a subset of the recognized
commands (call dev_project_info first to see what's available).
| Name | Required | Description | Default |
|---|---|---|---|
| run | No | ||
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 explicitly states that only DevTwin-recognized commands are ever executed (never arbitrary strings) and that each command has a timeout, which is valuable safety information. It doesn't mention side effects or output specifics, but the core behavioral constraints are transparent.
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 three sentences, front-loaded with the core purpose, then safety details, then usage guidance. Every sentence earns its place with specific, actionable information and no filler.
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 an output schema, so return values are documented separately. The description covers safety, usage flow, and available options, but omits any mention of the workspace parameter and doesn't clarify behavior when no checks are recognized. Given the tool's complexity and the availability of an output schema, these are minor 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?
Schema description coverage is 0%, so the description must compensate. It explains the 'run' parameter (restrict to a subset of recognized commands) and directs the agent to dev_project_info to see valid values. However, the 'workspace' parameter is completely unaddressed; an agent might not know it represents the working directory. The explanation for 'run' is helpful but not comprehensive for 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?
The description clearly states the tool runs recognized project checks (tests/lint) with concrete examples (pytest, gradlew test, npm test, cargo test). It uses a specific verb 'Run' and a well-defined resource, distinguishing it from sibling tools like dev_detect or dev_health which have different purposes.
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 advises calling dev_project_info first to discover available commands and to use the 'run' parameter to restrict execution to a subset. This provides clear context and a specific prerequisite, though it doesn't explicitly contrast with alternatives or state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_dependenciesC
Dependency state per detected ecosystem: package manager, lockfile presence/consistency signals, dependency counts, and whether packages appear installed locally (e.g. node_modules/, .venv/).
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description alone must disclose behavior. It describes output content but does not state whether the tool is read-only, scans the filesystem, requires network access, or has side effects. It also omits any performance or error implications. The behavior is not transparent.
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 filler. It front-loads the key concept and lists specifics efficiently. While it could be slightly more structured with an explicit verb, it is economical 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?
The tool has one undocumented parameter and no annotations. Even though an output schema exists (which may clarify return values), the description omits critical context about the workspace parameter and how ecosystems are detected. It is incomplete for an agent to call correctly without further inference.
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 'workspace' has zero schema description coverage (0%), and the description does not mention it at all. The agent has no idea what workspace does, how it affects the results, or what values are acceptable. The description fails to compensate for the undocumented 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 clearly states the tool reports dependency state per ecosystem, listing specific data points (package manager, lockfile, counts, local install). It is specific about content, though it lacks an explicit action verb like 'get' or 'list'. It does not differentiate from siblings, but the purpose is 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?
There is no guidance on when to use this tool versus alternatives like dev_detect, dev_health, or dev_drift. No mention of context that would trigger this tool, no exclusions, and no mention of alternatives. The agent must infer when to call it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_detectB
Detect what kind of project lives at workspace: languages, ecosystems,
and the evidence (files) behind each detection. Fast, file-based only.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It adds two meaningful traits: 'file-based only' (implying read-only, local operation) and 'fast' (performance). However, it lacks depth—no mention of error handling, what happens if the workspace is invalid, or whether it caches results. It provides some transparency but not comprehensive 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 two sentences with no fluff. It leads with the core purpose, then appends the behavioral notes. Every word contributes; nothing is redundant or unnecessary. This is an exemplary concise 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?
Given the tool's simplicity (one optional parameter, output schema exists), the description covers the essentials: what it does, the parameter, and some behavior. However, it omits usage context—when to choose this over siblings—and lacks deeper behavioral details like edge cases or side effects. It is adequate for basic use but not fully complete without referencing alternatives.
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. It explains 'workspace' as the location where the project lives, which clarifies the parameter's role. The default '.' is also consistent. While it doesn't detail accepted path formats, for a single optional parameter this is sufficient and adds meaning beyond 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 clearly states the verb 'detect' and the resource 'project at workspace', and specifies the outputs: languages, ecosystems, and evidence files. It distinguishes itself from a tautology and conveys a specific capability, though it does not explicitly contrast with sibling tools like dev_project_info.
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 alternatives. The phrase 'Fast, file-based only' hints at scope and performance but does not state when a developer should pick this over siblings such as dev_health or dev_project_info, nor does it mention exclusion criteria or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_driftA
Compare expected project state (required runtime/tool versions from project files) against what's actually installed locally. Reports each mismatch as a structured drift item.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 uses non-mutating verbs ('compare', 'reports') which strongly imply a read-only operation, but it does not explicitly state that it does not modify anything or disclose any side effects, permissions, or limitations. The lack of explicit transparency is a moderate gap for a tool that could potentially access local installations.
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 sentences with no unnecessary words. It front-loads the core comparison action and then specifies the output format. Every word contributes to understanding the tool's function and result.
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 a single optional parameter, an output schema, and no annotations, the description provides sufficient context to call it correctly. The purpose and result are clear, and the workspace parameter is implied. It could mention edge cases (e.g., invalid workspace) or what happens when no drift is found, but the output schema likely covers the return structure, so the description is nearly 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?
The schema describes 'workspace' with a default but no description, and schema description coverage is 0%. The description's mention of 'project files' and 'installed locally' gives context that the workspace parameter specifies which directory to inspect, but it does not explicitly describe the parameter's purpose or acceptable values. It adds some meaning but does not fully 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 clearly states the tool's verb (compare), resource (expected vs installed project state), and outcome (reports mismatch as structured drift items). It distinguishes itself from sibling tools by specifying the exact comparison of required runtime/tool versions against local installations, which is not ambiguous.
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 the intended use case (detecting version drift) but does not explicitly state when to prefer this tool over alternatives, nor does it mention any exclusions or conditions. With many siblings, explicit routing would be helpful, but the core purpose is clear enough that an agent can infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_explain_failureA
Diagnose a development failure. Give it the error/output text (and optionally the command that produced it); it cross-references project type, runtimes, services, ports, and dependencies to rank likely root causes with evidence -- never asserting certainty beyond the evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| command | No | ||
| workspace | Yes | ||
| error_message | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 that the tool 'cross-references project type, runtimes, services, ports, and dependencies' and that it 'ranks likely root causes with evidence'. It also clarifies a behavioral trait: 'never asserting certainty beyond the evidence'. This is transparent about the tool's inductive reasoning and its limitation. The description doesn't mention any destructive actions or side effects, but the name suggests (and the description confirms) it is purely diagnostic. Given the lack of annotations, this is a good level of 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 two sentences long, with the main purpose and inputs at the front. It packs a lot of information: the input, the cross-referencing behavior, the output (ranked root causes with evidence), and a critical limitation ('never asserting certainty beyond the evidence'). No fluff, every clause earns its place. The structure is clear: first sentence states the action, second expands on internals and output.
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 (diagnostic reasoning tool with multiple input parameters and an output schema), the description covers the core functionality well: it explains what the tool does, what it takes, and what it returns. The output schema exists (though not provided in the input schema here, but the signal says 'Has output schema: true'), so the description doesn't need to detail return values. The main missing piece is explicit mention of the 'workspace' parameter – it is implied by 'project type, runtimes, services' but not directly stated as an input. Other than that, the description is complete enough for an agent 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?
The schema description coverage is 0%, so the description must compensate for the parameters. The description mentions 'error/output text' and 'command that produced it' and 'workspace' implicitly. It doesn't explicitly name the workspace parameter, but it says 'cross-references project type, runtimes, services, ports, and dependencies' which implies the workspace is the project context. The description adds value by explaining what the parameters are used for (error_message as the primary input, command as optional context). It does not cover all parameter semantics in detail (e.g., format of workspace), but given the high-level nature, it provides enough meaning beyond the schema's bare property 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 clearly states the tool's purpose: diagnose a development failure. It specifies the inputs (error/output text, optionally the command) and the output (ranks likely root causes with evidence). The verb 'diagnose' and the resource 'development failure' are specific, and the description distinguishes it from siblings by focusing on failure analysis rather than detection, health, or project info.
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 tells the agent when to use this tool: when you have error/output text and optionally the command that produced it. It implies that the tool is for diagnosing failures, not for general health checks or detection. While it doesn't explicitly name alternatives, the context signals and sibling names (dev_check, dev_detect) suggest it's for failure analysis, and the description gives a clear condition for invocation. This is a strong usage guideline, essentially 'use this when you have error text and want root cause analysis'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_healthA
Full environment health check: combines project detection, runtime versions, dependency state, required services, ports, and Git state into a 0-100 health_score with concrete issues and recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It does convey that the tool is non-mutating (a 'check' that produces a score and issues), implying a read-only operation, but it stops short of explicitly stating side-effect-free behavior, performance implications, or what happens if the workspace doesn't exist. It also doesn't describe the structure of the 'issues and recommendations' it returns, though an output schema is present. The overall intent is clear, but some behavioral specifics are left to inference.
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, densely informative sentence that front-loads the core purpose ('Full environment health check') and immediately enumerates the covered areas. Every element earns its place—no filler, no repetition. It is compact while conveying both scope and outcome, making it highly efficient for an agent 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?
The description provides a good high-level overview of functionality and output, and the presence of an output schema relieves it of fully explaining return values. However, it omits any guidance on the `workspace` parameter (its purpose and valid values), which is necessary for correct invocation. It also doesn't mention prerequisites (e.g., must be inside a project) or how to interpret the health score beyond 'issues and recommendations'. For a tool that combines many aspects, these gaps are moderate, leaving the description slightly 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?
There is one parameter, `workspace` (default '.'), and the schema provides zero description coverage (0%). The tool description never mentions this parameter or explains its meaning (e.g., directory path, project root). Although the name is somewhat self-explanatory and there is a sensible default, the description fails to compensate for the missing schema documentation. The agent is left to infer what value to pass, which is a notable gap for a parameter that affects the scope of the health check.
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 verb ('health check') applied to the environment, lists the concrete aspects it combines (project detection, runtime versions, dependency state, services, ports, Git state), and defines the single 0-100 health_score output. It unambiguously distinguishes itself from narrower sibling tools like dev_dependencies or dev_services by being a 'Full environment health check' that aggregates many dimensions.
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 comprehensive, one-stop check by explicitly enumerating what it covers, which signals when to use it (when a holistic overview is needed). However, it does not explicitly exclude alternatives (e.g., 'use dev_dependencies for dependency-only checks') nor call out situations where a narrower tool is preferred. This is clear context without exclusions, so a 4 is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_precommitA
Read-only commit-readiness summary: Git status (dirty/staged/conflicts), project health, and staged files that look like secrets. Never commits, stages, or modifies anything.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It explicitly states 'Read-only' and 'Never commits, stages, or modifies anything.' This fully discloses the tool's side-effect-free nature, which is the critical behavioral trait. The description goes beyond a simple purpose statement by reassuring the agent of safety.
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 concise sentences, with the core purpose front-loaded. The first sentence states what it does, the second reinforces the read-only guarantee. No fluff or redundant details; every word adds value.
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 covers the tool's purpose, scope, and behavioral safeguards. An output schema exists, so return values are presumed documented elsewhere. The only notable gap is the undocumented 'workspace' parameter, which prevents the description from being fully complete for a drop-in usage understanding.
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 ('workspace') with 0% description coverage, and the tool description does not mention it at all. This leaves the agent to infer that 'workspace' refers to a path, but there is no explicit guidance, default behavior, or allowed values. The description 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 clearly states a specific verb+resource: 'Read-only commit-readiness summary' and details the specific checks (Git status, project health, secrets). It also explicitly states what it never does, which distinguishes it from mutation tools. The purpose is unambiguous and distinct from sibling dev_* tools.
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?
Usage context is implied by the name and description: it is for checking commit readiness, presumably before committing. However, the description does not explicitly state when to use this tool versus its siblings (e.g., dev_health, dev_check) or provide any exclusion criteria. It offers clear context but no explicit comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_prepareA
Produce a preparation plan for a (likely newly-cloned) repository: ordered steps to align runtimes, start required services, install dependencies, and run the project's build/test commands. This tool NEVER executes anything -- it only plans, and classifies each step's blast radius (read_only/safe/requires_approval/dangerous) for the caller.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full behavioral burden. It explicitly states 'This tool NEVER executes anything -- it only plans' — a critical guarantee that prevents an agent from expecting side effects. It also discloses the output behavior (classifying each step's blast radius). This is thorough and transparent for a planning tool with zero side effects.
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 sentences, front-loaded with the core purpose and then the key behavioral caveat. Every clause adds value: scope, typical use case, and the critical non-execution guarantee. No filler, no redundancy. It is concise and well-structured 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 its simplicity (one optional param, no execution), the description is almost complete. It covers what the plan includes (steps, blast radius classification) and what it never does. The output schema exists, so return details are covered implicitly. Minor omission: it doesn't specify the format or granularity of the plan, but that is not essential for invocation. Overall, an agent can confidently call 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?
The schema has a single optional 'workspace' parameter with 0% description coverage, so the description must compensate. It does not directly explain 'workspace', but the purpose 'for a repository' implies the workspace is the repository path. Since the parameter is simple, optional, and has a sensible default, the lack of explicit parameter explanation is a minor gap. This warrants a middle score.
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 ('Produce a preparation plan'), a specific resource ('repository'), and the scope ('align runtimes, start services, install deps, run build/test'). It also explicitly disclaims execution, which clearly distinguishes it from sibling tools like dev_check or dev_detect that likely run commands. The purpose is unambiguous and differentiated without needing to name siblings.
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 a (likely newly-cloned) repository' — and clarifies it is for planning, not execution. However, it does not explicitly state when to prefer this over sibling tools (e.g., dev_check for actual validation, dev_services for service management). The context is present but no alternatives or exclusions are given, so 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.
dev_project_infoA
Detailed project inspection: detected ecosystems, runtimes (installed vs required), build tools, test/build commands, environment variables in use, OS info, and Git state. Broader and slower than dev_detect.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 the scope of inspection and a performance characteristic (slower), but does not explicitly state read-only behavior, required permissions, or potential side effects. The word 'inspection' implies non-mutating, yet it is not stated outright.
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?
Two sentences that are front-loaded with the purpose and a detailed list, followed by a comparative note. Every word earns its place, with no redundancy or filler.
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?
An output schema exists, so return-value details are already covered. The description specifies a rich set of inspected aspects and includes a performance caveat. It does not explain the workspace parameter's role, but that is captured under parameter semantics. Overall, the description gives sufficient context for an agent to decide to call the tool and understand its high-level behavior.
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 'workspace' parameter at all. The parameter name is self-explanatory, but the description adds no contextual meaning about what value to provide (e.g., project root path) or how it affects the inspection.
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 specific verb 'inspection' with a specific resource ('project') and enumerates concrete content: ecosystems, runtimes, build tools, commands, environment variables, OS info, Git state. It also differentiates from dev_detect by noting 'broader and slower', making the purpose unmistakable.
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?
Explicitly contrasts with dev_detect ('Broader and slower than dev_detect'), giving a comparative usage cue. It implies choosing this tool when depth is needed over speed, though it does not list explicit when-not-to-use conditions or mention any other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dev_servicesA
Local service detection: which services (Postgres, Redis, and compose-defined services) this project appears to need, whether each is currently running/listening, and the evidence behind that conclusion.
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | . |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool performs local detection, checks whether services are running/listening, and provides evidence, which implies a read-only, non-destructive operation. However, it does not state that it has no side effects, does not start services, or what happens if services are missing. It is more transparent than a bare 'detect services' but still lacks explicit safety details.
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, dense sentence that front-loads the core purpose ('Local service detection') and packs in the three key outputs (needed services, running status, evidence). There is no filler or irrelevant information, making it highly 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 output schema exists (though not detailed here), so return values need not be explained. The description covers the essential scope: which services, whether running, and evidence. It omits explicit clarification of the 'workspace' parameter and any limitations, but for a detection tool with a single optional parameter, it is close to 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?
The schema has one parameter 'workspace' with a default of '.' and zero description coverage (0%). The description does not mention the parameter at all, leaving the agent to infer that it refers to the project directory. The name and default give some hint, but the description adds no semantic value beyond the schema, and the schema itself provides no description.
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 ('detection') and resource ('services'): it identifies which services (Postgres, Redis, compose-defined) the project needs, whether they are running, and provides evidence. This clearly distinguishes it from sibling tools like dev_dependencies (dependencies) or dev_health (health 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?
The description gives no explicit guidance on when to use this tool versus alternatives. It does not mention any conditions, alternatives, or exclusions. An agent would have to infer from the title and sibling names that this is about service detection, but there is no explicit routing or 'use this when' statement.
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. Dates show when Glama detected each change.
10 tool updates
v0.1.0- First observed
dev_check - First observed
dev_dependencies - First observed
dev_detect - First observed
dev_drift - First observed
dev_explain_failure - First observed
dev_health - First observed
dev_precommit - First observed
dev_prepare - First observed
dev_project_info - First observed
dev_services
TDQS
Each tool targets a distinct aspect of the development environment: detection, health, drift, failure diagnosis, project info, dependencies, services, checks, preparation, and precommit. Even similar tools like dev_detect and dev_project_info are clearly differentiated by scope and speed. There is no ambiguous overlap that would cause an agent to select the wrong tool.
All tool names follow the consistent pattern `dev_` + lower_snake_case, using descriptive verbs or nouns (detect, health, drift, explain_failure, etc.). The naming convention is uniform and predictable, with no mixing of camelCase or inconsistent verb styles.
With 10 tools, the server is well-scoped and each tool serves a clear purpose within the domain of development environment analysis and preparation. The count is within the ideal range and avoids both bloat and insufficient coverage.
The tool set covers the full lifecycle for a diagnostics/preparation server: detection, health assessment, drift checking, failure explanation, dependency and service checks, test execution, preparation planning, and precommit readiness. No obvious gaps exist for the stated purpose, and the tools work together to provide comprehensive environment insight.
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