Re:portFlow
OfficialRe:port Flow MCP
Offizieller Anzeigename: Re:port Flow MCP. Paket- und Implementierungskennung: reportflow-mcp. Legacy-Suchaliase: ReportFlow MCP Server und ReportFlow.
Übersicht
Ein MCP-Server (Model Context Protocol), der Ihre Re:port Flow-Vorlagen in PDF-Berichte verwandelt – Rechnungen, Verträge, Kontoauszüge, alles, was Sie gestaltet haben – direkt von Claude oder einem anderen MCP-kompatiblen KI-Agenten.
Related MCP server: PDF Tools AI MCP
Funktionen
PDFs aus natürlichsprachlichen Anfragen generieren, wie „Rechnung über $300 für Acme Corp erstellen“
Ihre Re:port Flow-Designs und deren Parameterschemata der KI direkt als MCP-Ressourcen zur Verfügung stellen.
Viele PDFs in einem Rutsch generieren und als einzelnes ZIP herunterladen
Ausgaben in dem Workspace-Ordner speichern, in dem sich der Benutzer gerade befindet (Claude Desktop / Claude Code / Cursor / VS Code werden unterstützt)
Einrichtung
Re:port Flow MCP läuft auf zwei Arten – wählen Sie die, die zu Ihrem Client passt.
Remote-Server (claude.ai / Web-Clients) – Streamable HTTP
Fügen Sie Re:port Flow als benutzerdefinierten Connector hinzu, der auf den gehosteten Endpunkt zeigt:
https://mcp.re-port-flow.com/mcpGehen Sie in Claude (claude.ai) zu Einstellungen → Connectors → Benutzerdefinierten Connector hinzufügen und fügen Sie die obige URL ein. Die Authentifizierung erfolgt In-App über OAuth (siehe Authentifizierung) – es muss nichts lokal installiert werden.
Lokaler Server (Claude Desktop / Claude Code / Cursor) – stdio über npx
Fügen Sie Folgendes zu Ihrer Konfigurationsdatei hinzu (.mcp.json, claude_desktop_config.json, ~/.cursor/mcp.json, usw.):
{
"mcpServers": {
"reportflow": {
"command": "npx",
"args": ["-y", "reportflow-mcp"]
}
}
}Das ist die gesamte Einrichtung. Keine Umgebungsvariablen, keine API-Schlüssel, keine Geheimnisse zu verwalten.
VS Code (MCP-fähige Builds)
Dasselbe JSON in .vscode/mcp.json.
Voraussetzungen
Remote: ein MCP-Client, der benutzerdefinierte HTTP-Connectors unterstützt (z. B. claude.ai). Keine lokale Installation.
Lokal (stdio): Node.js 22+ (wird von
npxautomatisch abgerufen) und ein Browser, der während der ersten Anmeldung verfügbar ist.Ein Re:port Flow-Konto (in beiden Fällen).
Unterstützte Protokollrevisionen
Beide Transporte (stdio / Streamable HTTP) bedienen zwei MCP-Protokollgenerationen von einem einzigen Endpunkt aus:
2026-07-28(aktuell) – zustandsloses Protokoll pro Anfrage. Moderne Clients erkennen es überserver/discover; kein Session-Header, Anfragen tragen ihre Protokollversion in_meta.Revisionen aus 2025 (
2025-11-25,2025-06-18,2025-03-26,2024-11-05,2024-10-07) – klassischerinitialize-Handshake, aus Gründen der Abwärtskompatibilität mit vorhandenen Clients (Claude Desktop, benutzerdefinierte Connectors von claude.ai, Cursor, ChatGPT, n8n, …) beibehalten.
Die Versionsauswahl ist bei beiden Transporten automatisch: Moderne Clients testen mit server/discover, Legacy-Clients senden weiterhin initialize – auf beiden Seiten ist keine Konfiguration erforderlich, und bestehende Verbindungen funktionieren unverändert weiter.
Authentifizierung
Remote (claude.ai)
Wenn Sie den Connector hinzufügen, führt Claude den OAuth-Ablauf für Sie aus: Anmelden → Arbeitsbereich wählen → zustimmen. Die Tokens werden vom Client aufbewahrt – es gibt keinen lokalen Schlüsselbund oder Browserschritt zu verwalten.
Lokal (stdio)
Laden Sie den MCP-Client neu und bitten Sie die KI:
Authentifizieren Sie sich bei Re:port Flow
Ein Browserfenster öffnet sich. Anmelden → Arbeitsbereich wählen → zustimmen, und schon sind Sie
fertig. Die Tokens werden im OS-Schlüsselbund gespeichert (macOS Keychain / Windows
Credential Manager / Linux libsecret), mit einem chmod-0600-Datei-Fallback, und
werden automatisch aktualisiert.
Verwendungsbeispiele
Jedes Beispiel unten ist ein Prompt, den Sie direkt einfügen können; die KI wählt die passenden Werkzeuge aus.
1. Ein einzelnes PDF generieren (Liste → Schema → Generieren)
Erstellen Sie mit der Rechnungsvorlage ein PDF für Acme Corp in Höhe von insgesamt $330.
Die KI listet Designs mit list_templates auf, ruft das Parameterschema mit
get_design_parameters ab, füllt die Werte ein und ruft generate_pdf_sync auf.
Remote: gibt eine Download-URL zurück (
fileUrl).Lokal: speichert die Datei außerdem und gibt ihren absoluten Pfad zurück.
2. Viele PDFs in einem Rutsch generieren
Generieren Sie mit der Kontoauszugsvorlage ein PDF pro Kunde (Acme $100, Globex $250, Initech $80) und geben Sie sie mir gemeinsam.
Lokal (stdio):
generate_pdfs_syncschreibt ein einzelnes ZIP in Ihren Workspace.Remote:
generate_pdfs_asyncführt den Batch aus und gibt eine Anforderungs-ID sowie eine Download-URL zurück.
3. Asynchron generieren und anschließend herunterladen (lokal)
Starten Sie das Vertrags-PDF im Hintergrund und laden Sie es dann herunter, sobald es fertig ist.
Die KI ruft generate_pdf_async auf (gibt sofort eine requestId zurück) und ruft dann
download_file auf, um das fertige PDF zu speichern. Das Pendant für Batches ist
generate_pdfs_async → download_zip. Diese Download-Werkzeuge gibt es nur unter stdio; auf
dem Remote-Server liefern die synchronen/asynchronen Werkzeuge bereits eine fileUrl zurück.
Tipp – Natürlichsprachliche Parameter: Auf einem Sampling-fähigen Client können Sie „Params für eine Rechnung über $1,000 an A社 entwerfen“ sagen, und die KI ruft
suggest_paramsauf, um die Kurzbeschreibung vor der Generierung in ein gültigesparams-Objekt umzuwandeln.
4. Ohne vorhandene Vorlagen starten (Galerie → Kopieren → Generieren)
Ich habe noch keine Vorlagen – erstellen Sie ein Rechnungs-PDF für Acme Corp.
Wenn list_templates leer ist, durchsucht die KI die öffentliche Vorlagengalerie
mit search_gallery_templates, zeigt Ihnen die Kandidaten, kopiert Ihre Auswahl
mit copy_gallery_template in Ihren Workspace und fährt dann mit dem
normalen Ablauf fort (get_design_parameters → generate_pdf_sync). Die Kopie landet immer
in dem Workspace, den Sie auf dem OAuth-Zustimmungsbildschirm ausgewählt haben – die KI kann
keinen anderen Workspace ansteuern.
Slash-Befehle
Befehl | Zweck |
| Schritt-für-Schritt-Rezept für ein einzelnes PDF |
| Rezept für die Batch-PDF-Generierung |
| Kurze Funktionstour |
Wo Dateien gespeichert werden (lokaler Modus)
Der Ausgabeort wird in dieser Reihenfolge bestimmt:
Explizite Anweisung des Benutzers (z. B. „auf meinem Desktop speichern“)
Das Wurzelverzeichnis des aktuell geöffneten Workspace (Claude Code / Cursor / VS Code)
Das temporäre Verzeichnis des Betriebssystems als Fallback
Referenz
Tools (von der KI aufgerufen)
Werkzeug | Zweck |
| Erstauthentifizierung / erneute Authentifizierung |
| Verfügbare Designs auflisten |
| Parameterschema für ein Design abrufen |
| Ein PDF erzeugen (sync gibt den Pfad zurück; async gibt eine Anfrage-ID zurück) |
| Mehrere PDFs erzeugen (gibt eine ZIP-Datei zurück) |
| Von asynchronen Werkzeugen erzeugte Artefakte herunterladen |
| Eine natürlichsprachliche Kurzbeschreibung mithilfe von MCP-Sampling in ein |
| Werkzeuge gemäß der ChatGPT-Konnektor-Konvention (ein einzelnes Zeichenfolgenargument), geschlossene Welt ( |
| Durchsuchen Sie die öffentliche Vorlagen-Galerie (keine Authentifizierung erforderlich) nach Schlüsselwort/-kategorie. Gibt Kandidatenvorlagen zurück, die noch nicht in Ihrem Arbeitsbereich sind – ihr |
| Vollständige Details einer öffentlichen Galerie-Vorlage anhand des |
| Schreibwerkzeug. Kopiert eine Galerie-Vorlage in den von Ihnen autorisierten Arbeitsbereich (der Zielarbeitsbereich ist durch Ihr Zugriffstoken festgelegt und kann nicht als Argument übergeben werden). Gibt |
Ressourcen (als KI-Kontext anhängbar)
URI | Inhalt |
| Liste der verfügbaren Designs |
| Parameterschema für ein Design |
| Katalog der Fehlermeldungen des Content Service |
| Übersicht der Serverfunktionen |
Prompts (Rezeptkarten für Slash-Befehle)
/generate_pdf, /generate_pdfs, /reportflow_help – Argumente übergeben; die KI folgt dem vorbereiteten Workflow.
Fehlerbehebung
Symptom | Lösung |
Fehler, der | Bitten Sie die KI: „Authentifizieren Sie sich erneut mit Re:port Flow“ |
|
|
Kein Schlüsselbund unter Linux verfügbar | Fällt automatisch auf eine |
Browser kann über SSH / Remote-Shell nicht geöffnet werden | Authentifizieren Sie sich einmal auf einem lokalen Computer; danach funktioniert das zwischengespeicherte Token auf entfernten Hosts |
Datenschutz
Re:port Flow MCP ist ein schlanker Client: Er leitet Ihre Anfragen an Ihr eigenes
Re:port Flow-Konto weiter und gibt die erzeugten PDFs zurück. Er verkauft oder teilt
Ihre Daten nicht mit Dritten. Authentifizierungstoken werden lokal gespeichert (Betriebssystem-Schlüsselbund,
oder eine chmod-0600-Datei als Fallback) und nur an die eigenen Dienste von Re:port Flow gesendet –
während des OAuth-Logins und als Bearer-Anmeldedaten bei jedem authentifizierten API-Aufruf
(Auflisten von Vorlagen, Erzeugen oder Herunterladen von PDFs). Sie werden niemals an Dritte weitergegeben.
Die vollständige Datenschutzerklärung – welche Daten erfasst werden, wie lange sie aufbewahrt werden und wie sie verarbeitet werden – finden Sie unter: lp.re-port-flow.com
Sicherheit
Der gehostete Endpunkt validiert den Host-Header (Schutz vor DNS-Rebinding) und
lehnt strukturell ungültige Origin-Header mit 403 Forbidden ab, gemäß den
Sicherheitsanforderungen der MCP-Streamable-HTTP-Spezifikation. Die Authentifizierung erfolgt
ausschließlich über ein Bearer-Token – keine Cookies, und CORS erlaubt niemals Anmeldedaten. Die
vollständige Richtlinie und ihr Bedrohungsmodell sind in
docs/security.md (Japanisch) dokumentiert.
Support
Sie brauchen Hilfe, haben einen Fehler gefunden oder eine Frage zur Verzeichnisprüfung?
Re:port Flow (Datenschutz & Support): https://lp.re-port-flow.com
GitHub Issues: https://github.com/re-port-flow/reportflow-mcp/issues
Lizenz
MIT – siehe LICENSE.
Links
Re:port Flow: https://re-port-flow.com
Datenschutz & Support: https://lp.re-port-flow.com
Issues: 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
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