Re:portFlow
OfficialRe:port Flow MCP
Официальное отображаемое имя: Re:port Flow MCP. Идентификатор пакета и реализации: reportflow-mcp. Устаревшие поисковые псевдонимы: ReportFlow MCP Server и ReportFlow.
Обзор
MCP-сервер (Model Context Protocol), который превращает ваши шаблоны Re:port Flow в PDF-отчёты — счета, договоры, выписки, всё, что вы разработали, — прямо из Claude или любого другого MCP-совместимого ИИ-агента.
Related MCP server: PDF Tools AI MCP
Возможности
Создавайте PDF по запросам на естественном языке, например: «создайте счёт для Acme Corp на сумму 300 долларов»
Предоставляйте ваши макеты Re:port Flow и их параметрические схемы напрямую ИИ в виде MCP-ресурсов
Массово создавайте много PDF и скачивайте их одним ZIP-архивом
Сохраняйте результаты в ту папку рабочего пространства, в которой сейчас находится пользователь (поддерживаются Claude Desktop / Claude Code / Cursor / VS Code)
Настройка
Re:port Flow MCP работает двумя способами — выберите тот, который подходит вашему клиенту.
Удалённый сервер (claude.ai / веб-клиенты) — Streamable HTTP
Добавьте Re:port Flow в качестве пользовательского коннектора, указывающего на размещённую конечную точку:
https://mcp.re-port-flow.com/mcpВ Claude (claude.ai) перейдите в Настройки → Коннекторы → Добавить пользовательский коннектор и вставьте указанный выше URL. Аутентификация выполняется в приложении через OAuth (см. Аутентификация) — ничего устанавливать локально не нужно.
Локальный сервер (Claude Desktop / Claude Code / Cursor) — stdio через npx
Добавьте следующее в ваш файл конфигурации (.mcp.json, claude_desktop_config.json, ~/.cursor/mcp.json и т. д.):
{
"mcpServers": {
"reportflow": {
"command": "npx",
"args": ["-y", "reportflow-mcp"]
}
}
}На этом вся настройка. Никаких переменных окружения, API-ключей и секретов, которыми нужно управлять.
VS Code (сборки с поддержкой MCP)
Тот же JSON в .vscode/mcp.json.
Требования
Удалённый режим: MCP-клиент, поддерживающий пользовательские HTTP-коннекторы (например, claude.ai). Локальная установка не требуется.
Локальный (stdio): Node.js 22+ (автоматически загружается через
npx) и браузер, доступный при первом входе.Учётная запись Re:port Flow (в любом случае).
Поддерживаемые версии протокола
Оба транспорта (stdio / Streamable HTTP) обслуживают два поколения протокола MCP из одной конечной точки:
2026-07-28(текущая) — протокол без состояния на каждый запрос. Современные клиенты обнаруживают его черезserver/discover; без заголовка сессии, запросы передают версию протокола в_meta.Ревизии эпохи 2025 года (
2025-11-25,2025-06-18,2025-03-26,2024-11-05,2024-10-07) — классическое рукопожатиеinitialize, сохранённое для обратной совместимости с существующими клиентами (Claude Desktop, пользовательские коннекторы claude.ai, Cursor, ChatGPT, n8n, …).
Выбор версии автоматический на обоих транспортах: современные клиенты отправляют запрос server/discover, устаревшие продолжают использовать initialize — никакой настройки не требуется ни с той, ни с другой стороны, а существующие подключения продолжают работать без изменений.
Аутентификация
Удалённый режим (claude.ai)
Когда вы добавляете коннектор, Claude выполняет за вас поток OAuth: Вход → выбор рабочего пространства → согласие. Токены хранятся у клиента — не нужно управлять локальной связкой ключей или шагом в браузере.
Локальный режим (stdio)
После перезагрузки MCP-клиента попросите ИИ:
Авторизуйтесь в Re:port Flow
Откроется окно браузера. Вход → выбор рабочего пространства → согласие — и готово. Токены хранятся в системной связке ключей (macOS Keychain / Windows Credential Manager / Linux libsecret), с запасным вариантом в виде файла с правами chmod-0600, и обновляются автоматически.
Примеры использования
Каждый пример ниже — это запрос, который можно вставить как есть; ИИ сам выбирает нужные инструменты.
1. Создание одного PDF (список → схема → генерация)
Используя шаблон счёта, создайте PDF для Acme Corp на сумму 330 долларов.
ИИ перечисляет макеты с помощью list_templates, получает схему параметров с помощью get_design_parameters, заполняет значения и вызывает generate_pdf_sync.
Удалённый режим: возвращает URL для скачивания (
fileUrl).Локальный режим: также сохраняет файл и возвращает его абсолютный путь.
2. Пакетное создание нескольких PDF
На основе шаблона выписки создайте по одному PDF для каждого клиента (Acme 100 долларов, Globex 250 долларов, Initech 80 долларов) и отдайте их мне вместе.
Локальный (stdio):
generate_pdfs_syncзаписывает один ZIP-архив в ваше рабочее пространство.Удалённый режим:
generate_pdfs_asyncзапускает пакет и возвращает идентификатор запроса и URL для скачивания.
3. Асинхронное создание с последующей загрузкой (локальный режим)
Запустите создание PDF договора в фоновом режиме, а затем скачайте его, когда он будет готов.
ИИ вызывает generate_pdf_async (сразу возвращает requestId), затем download_file для сохранения готового PDF. Пакетный аналог: generate_pdfs_async → download_zip. Эти инструменты загрузки доступны только через stdio; на удалённом сервере синхронные/асинхронные инструменты уже возвращают fileUrl.
Совет — параметры на естественном языке: в клиенте с поддержкой Sampling вы можете попросить «составьте params для счёта на 1000 долларов для A社», и ИИ вызовет
suggest_params, чтобы превратить бриф в корректный объектparamsперед генерацией.
4. Начало без шаблонов (галерея → копирование → генерация)
У меня пока нет шаблонов — создайте PDF счёта для Acme Corp.
Когда list_templates пуст, ИИ ищет в публичной галерее шаблонов с помощью search_gallery_templates, показывает вам кандидатов, копирует выбранный вами шаблон в ваше рабочее пространство с помощью copy_gallery_template, а затем продолжает обычным потоком (get_design_parameters → generate_pdf_sync). Копия всегда попадает в рабочее пространство, выбранное вами на экране согласия OAuth — ИИ не может нацелиться на другое рабочее пространство.
Слэш-команды
Команда | Назначение |
| Пошаговая инструкция для одного PDF |
| Инструкция для пакетной генерации PDF |
| Краткий обзор функций |
Где сохраняются файлы (локальный режим)
Расположение выходных файлов определяется в следующем порядке:
Явная инструкция от пользователя (например, «сохранить на мой рабочий стол»)
Корень текущего открытого рабочего пространства (Claude Code / Cursor / VS Code)
Системная временная папка как запасной вариант
Справочник
Инструменты (вызываемые ИИ)
Инструмент | Назначение |
| Первичная / повторная аутентификация |
| Список доступных дизайнов |
| Получить схему параметров для дизайна |
| Сгенерировать один PDF (sync возвращает путь; async возвращает ID запроса) |
| Сгенерировать несколько PDF-файлов (возвращает ZIP) |
| Скачать артефакты, созданные асинхронными инструментами |
| Преобразовать краткое описание на естественном языке в JSON |
| Инструменты конвенции коннектора ChatGPT (один строковый аргумент), закрытый мир ( |
| Поиск в публичной галерее шаблонов (без аутентификации) по ключевому слову/категории. Возвращает кандидатные шаблоны, которых ещё нет в вашем рабочем пространстве — их |
| Получить полные сведения об одном шаблоне публичной галереи по |
| Инструмент записи. Копирует шаблон галереи в авторизованное вами рабочее пространство (целевое рабочее пространство фиксируется вашим токеном доступа и не может быть передано аргументом). Возвращает |
Ресурсы (прикрепляемые как контекст ИИ)
URI | Содержимое |
| Список доступных дизайнов |
| Схема параметров для одного дизайна |
| Каталог сообщений об ошибках от Content Service |
| Обзор возможностей сервера |
Промпты (карточки рецептов слэш-команд)
/generate_pdf, /generate_pdfs, /reportflow_help — передайте аргументы, и ИИ следует подготовленному рабочему процессу.
Устранение неполадок
Симптом | Решение |
Ошибка, содержащая | Попросите ИИ: "переаутентифицируйтесь с Re:port Flow" |
|
|
Нет доступной связки ключей в Linux | Автоматически переключается на файл с правами chmod-0600 в |
Браузер не может открыться через SSH / удалённую оболочку | Выполните аутентификацию один раз на локальной машине; после этого кэшированный токен работает на удалённых хостах |
Конфиденциальность
Re:port Flow MCP — это тонкий клиент: он пересылает ваши запросы в ваш собственный
аккаунт Re:port Flow и возвращает сгенерированные PDF. Он не продаёт и не передаёт
ваши данные третьим лицам. Токены аутентификации хранятся локально (в связке ключей
ОС или в запасном файле с правами chmod-0600) и отправляются только в собственные
сервисы Re:port Flow — при входе через OAuth и как учётные данные Bearer в каждом
аутентифицированном API-вызове (список шаблонов, генерация или загрузка PDF). Они
никогда не передаются третьим лицам.
Для полной политики конфиденциальности — что собирается, как долго хранится и как обрабатывается — см.: lp.re-port-flow.com
Безопасность
Размещённая конечная точка проверяет заголовок Host (защита от DNS-ребендинга) и
отклоняет структурно недопустимые заголовки Origin с ответом 403 Forbidden, согласно
требованиям безопасности спецификации MCP Streamable HTTP. Аутентификация — только
Bearer-токеном: без cookie, и CORS никогда не разрешает учётные данные. Полная
политика и её модель угроз описаны в docs/security.md (на японском языке).
Поддержка
Нужна помощь, нашли ошибку или есть вопрос по проверке каталога?
Re:port Flow (конфиденциальность и поддержка): https://lp.re-port-flow.com
GitHub Issues: https://github.com/re-port-flow/reportflow-mcp/issues
Лицензия
MIT — см. LICENSE.
Ссылки
Re:port Flow: https://re-port-flow.com
Конфиденциальность и поддержка: https://lp.re-port-flow.com
Проблемы: https://github.com/re-port-flow/reportflow-mcp/issues
Available Tools
10 toolsauthenticateAInspect
ReportFlow への OAuth2 認証を行います。ブラウザが起動し、ログイン・ワークスペース選択・consent を経てトークンを keychain (または XDG file) に保存します。他のツールが認証エラーを返したら、まずこのツールを呼んでください。force=true で既存トークンを破棄して再認証します。
| Name | Required | Description | Default |
|---|---|---|---|
| force | No | 既存トークンを破棄して再認証する場合 true |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the full authentication flow (browser launch, login, workspace selection, consent, token storage) and aligns with annotations (destructiveHint=false, openWorldHint=true). Adds valuable behavioral context beyond 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?
Two concise sentences front-loading the main action, with no wasted words. Efficiently structured.
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 one-parameter tool with no output schema, the description fully covers the authentication process, usage context, and parameter behavior. Complete and sufficient.
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%, and the description's mention of force parameter essentially paraphrases the schema's description. Minimal additional value beyond what schema provides.
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?
Description clearly states the tool performs OAuth2 authentication to ReportFlow, including browser launch, token storage, and distinct action from siblings which handle downloads and PDF generation.
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 instructs to call this tool first when other tools return authentication errors, and explains when to use force=true for re-authentication. Provides clear context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
download_fileAIdempotentInspect
generate_pdf_asyncで生成した単一PDFファイルをダウンロードします。requestIdとfileIdを指定し、ローカルファイルパスを返します。outputDir を指定するとそのディレクトリに、未指定の場合は現在の作業ディレクトリに保存します。
| Name | Required | Description | Default |
|---|---|---|---|
| requestId | Yes | generate_pdf_asyncで返されたrequestId(UUID) | |
| fileId | Yes | generate_pdf_asyncのfiles[].fileId | |
| fileName | No | 保存ファイル名(省略時はfileId.pdf) | |
| outputDir | No | 出力先ディレクトリ (相対/絶対)。未指定時はクライアントのワークスペース (Roots) または現在の作業ディレクトリに保存。ユーザーが場所を指定した場合のみセットすること。 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that files are saved locally, returns the file path, and handles directory selection. Annotations already indicate idempotency, and the description adds context about default behavior. No contradictions.
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, front-loaded with the action, no extraneous information. Efficient and complete.
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 prerequisite, parameters, output, and directory behavior. No output schema needed; the return value is explained. Complete given 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 coverage is 100%, but the description adds value by explaining the default directory behavior (current working directory) not present in schema. All 4 parameters are well-covered.
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 downloads a single PDF file generated by generate_pdf_async, specifying the required parameters and return value. It distinguishes from the sibling download_zip.
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 explains that this tool is used after generate_pdf_async and describes the optional outputDir. It does not explicitly exclude cases where download_zip might be preferred, but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
download_zipAIdempotentInspect
generate_pdfs_asyncで生成したZIPファイルをダウンロードします。requestIdを指定し、ローカルのZIPファイルパスを返します。outputDir を指定するとそのディレクトリに、未指定の場合は現在の作業ディレクトリに保存します。
| Name | Required | Description | Default |
|---|---|---|---|
| requestId | Yes | generate_pdfs_asyncで返されたrequestId(UUID) | |
| fileName | No | 保存ファイル名(省略時はrequestId.zip) | |
| outputDir | No | 出力先ディレクトリ (相対/絶対)。未指定時はクライアントのワークスペース (Roots) または現在の作業ディレクトリに保存。ユーザーが場所を指定した場合のみセットすること。 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide idempotentHint=true and non-destructive nature. The description adds that it saves to a directory and returns a local path, which is useful but not extensive. No contradictions.
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, front-loaded with the action, no unnecessary words. Every sentence 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?
For a simple download tool, the description covers how to use it, what to specify, and what it returns. No output schema, but the return is implied. Could mention that it overwrites existing files, but not essential.
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%, but the description adds context beyond schema: it explains the behavior of outputDir (saves to current directory if unspecified). This adds value, though fileName is not mentioned.
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 downloads a ZIP file generated by generate_pdfs_async, specifies requestId, and returns a local path. This distinguishes it from siblings like download_file.
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 ties the tool to a specific prior tool (generate_pdfs_async), giving clear context. However, it does not explicitly mention when not to use or list alternatives beyond that association.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_pdf_asyncAInspect
デザインIDとパラメータを指定してPDFを非同期生成します。即座にrequestIdとfiles情報を返します。ファイルのダウンロードはdownload_fileツールを使用してください。
【重要】呼び出し前に必ず get_design_parameters でデザインの必要パラメータ構造を確認し、ユーザーから必要な値を聞き出すこと。ユーザーが指定していないパラメータがある場合は、本ツールを呼ぶ前にユーザーに必ず確認すること。プレースホルダー値・架空の値を勝手に生成しないこと。パラメータが一切提供されていない場合も、まずユーザーに値を尋ねること。
| Name | Required | Description | Default |
|---|---|---|---|
| designId | Yes | デザインID(UUID形式) | |
| version | Yes | デザインバージョン番号 | |
| content | Yes | PDF生成コンテンツ |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are minimal (readOnlyHint false, etc.). Description adds that it's async and returns immediately, but lacks details on side effects, idempotency, or error 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?
Two efficient paragraphs: first states purpose, second gives critical usage guidelines. No redundancy, front-loaded with key info.
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?
Returns requestId and files info are mentioned but not detailed. No output schema, so description could elaborate further on response format or error handling. Links to download_file and get_design_parameters partially compensates.
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 covers all parameters with detailed descriptions. Description adds crucial guidance to check parameter structure with get_design_parameters, adding value beyond 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?
Description clearly states the tool asynchronously generates PDF with design ID and parameters, returns requestId and files info, and distinguishes from sibling tools like download_file and synchronous variants.
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 explicit pre-conditions: must call get_design_parameters, ask user for missing values, avoid placeholder values. Does not mention alternative generation tools (synchronous, batch) that could be compared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_pdfs_asyncAInspect
複数のパラメータセットでPDFを一括非同期生成します。即座にrequestIdとfiles情報を返します。ZIPダウンロードはdownload_zipツールを使用してください。
【重要】呼び出し前に必ず get_design_parameters でデザインの必要パラメータ構造を確認し、ユーザーから必要な値を聞き出すこと。ユーザーが指定していないパラメータがある場合は、本ツールを呼ぶ前にユーザーに必ず確認すること。プレースホルダー値・架空の値を勝手に生成しないこと。パラメータが一切提供されていない場合も、まずユーザーに値を尋ねること。
| Name | Required | Description | Default |
|---|---|---|---|
| designId | Yes | デザインID(UUID形式) | |
| version | Yes | デザインバージョン番号 | |
| contents | Yes | PDF生成コンテンツの配列(複数ファイル) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false (write operation) and destructiveHint=false. The description adds behavioral context: 'Immediately returns requestId and files information,' clarifying the async nature and immediate response. It does not contradict 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 concise, front-loaded with the primary function, and contains no unnecessary words. The important warning section is separate and clearly marked. Every sentence 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?
Given the tool's complexity (multiple PDFs async), the description covers key aspects: async behavior, immediate return, prerequisite steps, and referral to another tool for ZIP. It lacks detail on the response structure beyond 'requestId and files information,' but this is adequate given no output schema. Annotations and schema fill remaining 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 coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining that the 'params' field should be structured based on get_design_parameters, and it highlights the required 'fileName' and 'params' fields. This guidance is crucial 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 clearly states the tool's purpose: 'Generates multiple PDFs asynchronously with multiple parameter sets.' It specifies the verb 'generate', the resource 'multiple PDFs', and the asynchronous mode. It also distinguishes from siblings by explicitly mentioning the download_zip tool for ZIP downloads and implying sync versions exist.
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 explicit usage guidelines: before calling, use get_design_parameters to check required parameters, ask the user for missing values, and never generate placeholders. It also directs the user to download_zip for ZIP downloads, offering clear when-to-use versus alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_pdfs_syncAInspect
複数のパラメータセットでPDFを一括同期生成し、ZIPファイルとして返します。生成完了後にZIPファイルのローカルパスを返します。outputDir を指定するとそのディレクトリに、未指定の場合はクライアントのワークスペース (Roots) または OS 一時ディレクトリに保存します。zipFileName で出力 ZIP のファイル名を指定可能 (デフォルト download.zip)。
【重要】呼び出し前に必ず get_design_parameters でデザインの必要パラメータ構造を確認し、ユーザーから必要な値を聞き出すこと。ユーザーが指定していないパラメータがある場合は、本ツールを呼ぶ前にユーザーに必ず確認すること。プレースホルダー値・架空の値を勝手に生成しないこと。パラメータが一切提供されていない場合も、まずユーザーに値を尋ねること。
| Name | Required | Description | Default |
|---|---|---|---|
| designId | Yes | デザインID(UUID形式) | |
| version | Yes | デザインバージョン番号 | |
| contents | Yes | PDF生成コンテンツの配列(複数ファイル) | |
| outputDir | No | 出力先ディレクトリ (相対/絶対)。未指定時はクライアントのワークスペース (Roots) または現在の作業ディレクトリに保存。ユーザーが場所を指定した場合のみセットすること。 | |
| zipFileName | No | 出力 ZIP のファイル名 (省略時は download.zip) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint=false, destructiveHint=false), the description details synchronous generation, local path return, output directory logic, and shareType mapping. It also warns about not fabricating parameter values, adding behavioral 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 paragraphs: first explains functionality and output, second is an important usage note. Every sentence adds value, no redundancy, key information is front-loaded.
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 core purpose, requirements, output, and configuration options. It could mention potential limitations like file size or error handling, but for its complexity it is sufficiently 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%, but the description adds value by explaining shareType codes and their response mapping, default output directory behavior, and that 'params' should be obtained via get_design_parameters. This goes beyond the raw schema definitions.
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 explicitly states the tool generates multiple PDFs synchronously from parameter sets and returns a ZIP file. It distinguishes from siblings like generate_pdf_sync (single) and generate_pdfs_async (async) by specifying '一括同期生成' (batch sync generation).
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 clear prerequisites: always call get_design_parameters first and ask the user for missing values. It warns against using placeholder values. However, it does not explicitly contrast with async tools 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.
generate_pdf_syncAInspect
デザインIDとパラメータを指定してPDFを生成します。応答にダウンロード URL が含まれるため、本ツール 1 回の呼び出しで結果提示が完結します (別途ダウンロード用ツールを呼ぶ必要はありません)。
stdio モード (Claude Desktop / Code): ローカルに保存し絶対パスも返します。outputDir で保存先を指定できます (未指定時はクライアントのワークスペース Roots または OS 一時ディレクトリ)。
HTTP モード (claude.ai / n8n 等): サーバー側には保存しません。includePreview=true を指定すると inline preview 用のバイナリも併せて返します (claude.ai が PDF preview をサポートしていない現状ではデフォルト false 推奨)。
【重要】呼び出し前に必ず get_design_parameters でデザインの必要パラメータ構造を確認し、ユーザーから必要な値を聞き出すこと。プレースホルダー値・架空の値を勝手に生成しないこと。
| Name | Required | Description | Default |
|---|---|---|---|
| designId | Yes | デザインID(UUID形式) | |
| version | Yes | デザインバージョン番号 | |
| content | Yes | PDF生成コンテンツ | |
| outputDir | No | 出力先ディレクトリ (相対/絶対)。未指定時はクライアントのワークスペース (Roots) または現在の作業ディレクトリに保存。ユーザーが場所を指定した場合のみセットすること。 | |
| includePreview | No | true 指定時のみ EmbeddedResource (application/pdf, base64 blob) を応答に含める。claude.ai は現状 PDF resource を inline 表示しないため、通常は省略 (false) で fileUrl のみを利用するのが効率的。 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds significant behavioral context beyond annotations: synchronous generation, download URL in response, local save for stdio, no server save for HTTP, optional inline preview. No contradictions with annotations (readOnlyHint=false, destructiveHint=false).
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?
Well-structured with bullet points for modes and warnings. Front-loaded key info. Slightly verbose but each part adds value. Could be marginally shorter but still 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?
Covers all aspects: pre-condition (check parameters), post-condition (download URL, local save), mode-specific details, parameter constraints. No output schema, but response description is sufficient. Comprehensive for a complex tool with nested object.
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%, but description adds critical context: outputDir default behavior, includePreview only when needed, params must come from get_design_parameters, shareType codes mapping. Enhances understanding beyond 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 generates a PDF synchronously with a download URL. It distinguishes from async siblings and download tools, and explains mode-specific behavior (stdio vs HTTP). 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?
Explicitly instructs to call get_design_parameters first, ask user for values, and avoid placeholder/fake data. Also provides when to use includePreview and outputDir. Differentiates from async tools and download tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_design_parametersARead-onlyIdempotentInspect
デザインテンプレートのパラメータ構造を取得します。帳票生成に必要なパラメータの型・構造を確認できます。
| Name | Required | Description | Default |
|---|---|---|---|
| designId | Yes | デザインID(UUID形式) | |
| version | No | バージョン番号(省略時は最新版) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint, so the description adds context about what specific information is retrieved (types/structures). No additional behavioral traits beyond 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?
Two concise sentences with front-loaded key information. No extraneous words.
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 read-only tool with two parameters and comprehensive annotations, the description fully covers necessary context. No output schema needed as return is straightforward.
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 clear descriptions for both parameters. Description does not add meaning beyond what the schema provides, so baseline of 3 is appropriate.
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?
Description clearly states the tool retrieves parameter structure of design templates. It specifically uses verb 'get' and resource 'design template parameters', distinguishing it from sibling tools like generate_pdf_* or list_templates.
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?
Implies usage for inspecting parameter structure before form generation, but does not explicitly state when to use or alternatives. No exclusions or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_templatesARead-onlyIdempotentInspect
ワークスペース内のデザイン一覧を取得します。各デザインのID・名称・最新バージョン・サムネイルURLを返します。取得したidをdesignIdとしてPDF生成ツールやget_design_parametersに使用します。
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint true, indicating safe, idempotent behavior. The description adds value by specifying the exact return fields (ID, name, version, thumbnail URL) and the purpose of the output, which is not covered by 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?
Description is extremely concise: two sentences, no filler. First sentence states the core function and output, second sentence provides usage guidance. Perfectly front-loaded 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?
Given no parameters and no output schema, the description fully covers what the tool does, what it returns, and how to use the result. No missing information for an agent to correctly invoke and utilize the 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?
Tool has zero parameters, so schema coverage is 100%. Baseline score of 4 applies as the description does not need to add parameter information.
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?
Description clearly states the tool retrieves a list of designs in the workspace and specifies the returned fields (ID, name, version, thumbnail URL). It also explains how to use the IDs with downstream tools, differentiating its purpose from 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?
Description explains that the obtained ID should be used as designId for PDF generation and get_design_parameters. It provides clear context for when to use the tool, though it does not explicitly list when not to use it or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
suggest_paramsARead-onlyInspect
自然文の要件と designId からクライアント AI(Sampling)を使って generate_pdf_sync の params JSON を組み立てます。サーバー側 API キー不要。Sampling 未対応クライアントでは利用不可です。生成された params は内容確認のうえユーザーの承認を得てから generate_pdf_sync に渡してください。
| Name | Required | Description | Default |
|---|---|---|---|
| designId | Yes | デザインID(UUID形式) | |
| version | No | バージョン番号(省略時は最新版) | |
| description | Yes | 帳票の内容を自然文で記述(例: "請求書、宛先A社、合計1万円") |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true. The description adds context about client-side AI (Sampling), no server API key needed, and the need for user approval, enhancing transparency beyond 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 paragraph that conveys essential information efficiently, though it could be slightly more structured for easier parsing.
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, the description explains the output purpose. It covers prerequisites (Sampling), workflow (user approval), and usage context, making it fairly complete for a utility 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 covers all parameters with descriptions. The description does not add significant new semantics beyond implying description is natural language, so baseline score 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 clearly states the tool assembles params JSON for generate_pdf_sync using natural language and designId via Sampling. It distinguishes from sibling tools like generate_pdf_sync and is specific about its role.
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 states it is not usable on clients without Sampling support and instructs to get user approval before passing to generate_pdf_sync, providing clear usage context.
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.
10 tool updates
v0.1.0- First observed
authenticate - First observed
download_file - First observed
download_zip - First observed
generate_pdf_async - First observed
generate_pdf_sync - First observed
generate_pdfs_async - First observed
generate_pdfs_sync - First observed
get_design_parameters - First observed
list_templates - First observed
suggest_params
TDQS
Scored across 10 tools
Each tool has a clear, distinct purpose. Authentication is separate, sync vs async generators are clearly labeled, download tools are paired with async generators, and the helper tools (list_templates, get_design_parameters, suggest_params) are unique. No overlap.
All tool names follow a consistent verb_noun pattern in snake_case. Variations like generate_pdf_async vs generate_pdf_sync are systematic and predictable, making it easy to understand the tool's function from its name.
10 tools is an ideal size for this domain. It covers authentication, template exploration, parameter retrieval, PDF generation (sync/async, single/batch), downloading results, and smart param suggestion—all essential without unnecessary bloat.
The tool set provides a complete workflow for generating PDFs from templates: authenticate, list templates, get parameters, generate (sync or async, single or batch), and download. The inclusion of suggest_params adds convenience. No obvious gaps for the stated purpose.
Maintenance
Related MCP Connectors
Generate PDFs from templates via AI chat. Works with Claude, ChatGPT, Cursor, and any MCP client.
DocBase MCP server for AI agents
LLM Orchestration MCP Agent
Hosted MCP server: convert PDFs to clean, LLM-ready Markdown with tables, formulas and OCR.
Related MCP Servers
- AlicenseBqualityCmaintenanceMCP server that converts Markdown to high-quality PDF documents using LaTeX, enabling AI agents like Claude to generate professional PDFs without requiring sign-ups or credit cards.144 npm11MIT
- AlicenseNot gradedqualityBmaintenancePDF Tools AI - MCP server providing AI-powered tools and automation by MEOK AI Labs7 npmMIT
- AlicenseAqualityFmaintenanceMCP server for BulkRender — generate bulk DOCX and PDF documents from Claude, Cursor, Windsurf, and any MCP-compatible AI assistant1551 npm1MIT
- AlicenseAqualityCmaintenanceMCP server for generating professional PDFs from structured JSON in AI agents like Claude or Cursor, using pure Node.js with embedded fonts and precision text layout.621 npmMIT