Re:portFlow
OfficialRe:portFlow MCP is a server that generates PDF reports (invoices, contracts, receipts, etc.) from ReportFlow templates via AI agents like Claude, Cursor, or VS Code.
Authentication
Authenticate with ReportFlow via OAuth 2.0 (browser-based login with Authorization Code + PKCE); supports forced re-authentication to reset tokens.
Template Discovery
List all design templates in your workspace (IDs, names, versions, thumbnails).
Retrieve the parameter schema for a specific template to understand required inputs before generating.
PDF Generation
Generate a single PDF synchronously or asynchronously using a design ID and parameters.
Bulk-generate multiple PDFs in one call (sync or async), returned as a ZIP archive.
Use AI sampling to translate natural-language descriptions into structured parameters for generation.
File Download
Download async-generated PDFs or batch ZIP archives to a local path using a
requestId.In remote mode, generation returns download URLs; in stdio mode, files are saved directly to local workspace.
Sharing & Access Control
Generated PDFs can be shared as workspace-only, invite-only, or public URL (optionally passcode-protected).
Execution Modes
Remote HTTP endpoint (for Claude.ai web, etc.) or local stdio (for Claude Desktop, Cursor, VS Code, etc.).
Re:port Flow MCP
Official display name: Re:port Flow MCP. Package and implementation identifier: reportflow-mcp. Legacy search aliases: ReportFlow MCP Server and ReportFlow.
Overview
An MCP (Model Context Protocol) server that turns your Re:port Flow templates into PDF reports — invoices, contracts, statements, anything you've designed — straight from Claude or any other MCP-compatible AI agent.
Related MCP server: PDF Tools AI MCP
Features
Generate PDFs from natural-language requests like "create an invoice for Acme Corp totalling $300"
Expose your Re:port Flow designs and their parameter schemas directly to the AI as MCP Resources
Bulk-generate many PDFs and download them as a single ZIP
Save outputs to whichever workspace folder the user is currently in (Claude Desktop / Claude Code / Cursor / VS Code all supported)
Setup
Re:port Flow MCP runs in two ways — pick whichever matches your client.
Remote server (claude.ai / web clients) — Streamable HTTP
Add Re:port Flow as a custom connector pointing at the hosted endpoint:
https://mcp.re-port-flow.com/mcpIn Claude (claude.ai) go to Settings → Connectors → Add custom connector and paste the URL above. Authentication is handled in-app via OAuth (see Authentication) — nothing to install locally.
Local server (Claude Desktop / Claude Code / Cursor) — stdio via npx
Add the following to your config file (.mcp.json, claude_desktop_config.json, ~/.cursor/mcp.json, etc.):
{
"mcpServers": {
"reportflow": {
"command": "npx",
"args": ["-y", "reportflow-mcp"]
}
}
}That's the whole setup. No env vars, no API keys, no secrets to manage.
VS Code (MCP-enabled builds)
Same JSON in .vscode/mcp.json.
Requirements
Remote: an MCP client that supports custom HTTP connectors (e.g. claude.ai). No local install.
Local (stdio): Node.js 22+ (auto-fetched by
npx) and a browser available during the first login.A Re:port Flow account (either way).
Supported protocol revisions
Both transports (stdio / Streamable HTTP) serve two MCP protocol generations from a single endpoint:
2026-07-28(current) — stateless per-request protocol. Modern clients discover it viaserver/discover; no session header, requests carry their protocol version in_meta.2025-era revisions (
2025-11-25,2025-06-18,2025-03-26,2024-11-05,2024-10-07) — classicinitializehandshake, kept for backwards compatibility with existing clients (Claude Desktop, claude.ai custom connectors, Cursor, ChatGPT, n8n, …).
Version selection is automatic on both transports: modern clients probe with server/discover, legacy clients keep sending initialize — no configuration is required on either side, and existing connections keep working unchanged.
Authentication
Remote (claude.ai)
When you add the connector, Claude runs the OAuth flow for you: Sign in → pick a workspace → consent. Tokens are held by the client — there's no local keychain or browser step to manage.
Local (stdio)
After reloading the MCP client, ask the AI:
Authenticate with Re:port Flow
A browser window opens. Sign in → pick a workspace → consent, and you're
done. Tokens are stored in your OS keychain (macOS Keychain / Windows
Credential Manager / Linux libsecret), with a chmod-0600 file fallback, and
are refreshed automatically.
Usage examples
Each example below is a prompt you can paste as-is; the AI picks the right tools.
1. Generate a single PDF (list → schema → generate)
Using the invoice template, create a PDF for Acme Corp totalling $330.
The AI lists designs with list_templates, fetches the parameter schema with
get_design_parameters, fills in the values, and calls generate_pdf_sync.
Remote: returns a download URL (
fileUrl).Local: also saves the file and returns its absolute path.
2. Batch-generate many PDFs
From the statement template, generate one PDF per customer (Acme $100, Globex $250, Initech $80) and give them to me together.
Local (stdio):
generate_pdfs_syncwrites a single ZIP to your workspace.Remote:
generate_pdfs_asyncruns the batch and returns a request id plus a download URL.
3. Async generate, then download (local)
Kick off the contract PDF in the background, then download it once it's ready.
The AI calls generate_pdf_async (returns a requestId immediately), then
download_file to save the finished PDF. The batch equivalent is
generate_pdfs_async → download_zip. These download tools are stdio-only; on
the remote server the sync/async tools already return a fileUrl.
Tip — natural-language params: on a Sampling-capable client you can ask "draft the params for a $1,000 invoice to A社" and the AI will call
suggest_paramsto turn the brief into a validparamsobject before generating.
4. Start from zero templates (gallery → copy → generate)
I don't have any templates yet — create an invoice PDF for Acme Corp.
When list_templates is empty, the AI searches the public template gallery
with search_gallery_templates, shows you the candidates, copies your pick
into your workspace with copy_gallery_template, and then proceeds with the
normal flow (get_design_parameters → generate_pdf_sync). The copy always
lands in the workspace you selected on the OAuth consent screen — the AI
cannot target any other workspace.
Slash commands
Command | Purpose |
| Step-by-step recipe for a single PDF |
| Recipe for batch PDF generation |
| Quick feature tour |
Where files are saved (local mode)
Output location is resolved in this order:
Explicit instruction from the user (e.g. "save to my Desktop")
The currently-open workspace root (Claude Code / Cursor / VS Code)
The OS temp directory as fallback
Build your own agent
The setup above assumes an MCP client that manages its own connection and login. If you are writing the agent yourself — Hugging Face Agents, a custom tool loop, or raw HTTP — the hosted endpoint is open to you directly:
https://mcp.re-port-flow.com/mcpagents.md — the agent-facing guide: transport details, the ten HTTP tools, the OAuth flow, model selection, and the rules an agent has to follow (never invent business data;
copy_gallery_templateis not idempotent;passthroughvalues end up in the PDF's metadata).examples/ — runnable Python, JavaScript and curl clients, plus a script that walks the OAuth flow and prints an access token.
initialize and tools/list work without credentials, so you can discover the
toolset before wiring up authentication. Every tools/call needs a Bearer token.
Hugging Face
Re:port Flow is on the Hub at
huggingface.co/reportflow. The Hugging
Face SDKs have no MCP OAuth flow of their own, so fetch a token once with
examples/oauth/get-token.sh and inject it as
an Authorization header — see
examples/python/hf_mcp_client.py and
examples/javascript/hf-mcp-client.mjs.
Reference
Tools (called by the AI)
Tool | Purpose |
| First-time / re-authentication |
| List available designs |
| Fetch the parameter schema for a design |
| Generate one PDF (sync returns path; async returns request ID) |
| Generate many PDFs (returns a ZIP) |
| Download artifacts produced by async tools |
| Translate a natural-language brief into a |
| ChatGPT connector convention tools (single string argument), closed-world ( |
| Search the public template gallery (no auth needed) by keyword/category. Returns candidate templates that are not yet in your workspace — their |
| Fetch full details of one public gallery template by |
| Write tool. Copy a gallery template into the workspace you authorized (the target workspace is fixed by your access token and cannot be passed as an argument). Returns |
Resources (attachable as AI context)
URI | Contents |
| List of available designs |
| Parameter schema for one design |
| Catalog of error messages from the Content Service |
| Server feature overview |
Prompts (slash-command recipe cards)
/generate_pdf, /generate_pdfs, /reportflow_help — pass arguments and the AI follows the prepared workflow.
Troubleshooting
Symptom | Fix |
Error containing | Ask the AI: "re-authenticate with Re:port Flow" |
|
|
No keychain available on Linux | Falls back automatically to a chmod-0600 file under |
Browser cannot open over SSH / remote shell | Authenticate once on a local machine; afterwards the cached token works on remote hosts |
Privacy
Re:port Flow MCP is a thin client: it forwards your requests to your own
Re:port Flow account and returns the generated PDFs. It does not sell or share
your data with third parties. Authentication tokens are stored locally (OS
keychain, or a chmod-0600 file fallback) and are sent only to Re:port Flow's
own services — during the OAuth login, and as a Bearer credential on each
authenticated API call (listing templates, generating or downloading PDFs).
They are never shared with any third party.
For the full privacy policy — what is collected, how long it is retained, and how it is handled — see: lp.re-port-flow.com
Security
The hosted endpoint validates the Host header (DNS-rebinding protection) and
rejects structurally invalid Origin headers with 403 Forbidden, per the MCP
Streamable HTTP specification's Security requirements. Authentication is
Bearer-token only — no cookies, and CORS never allows credentials. The full
policy and its threat model are documented in
docs/security.md (Japanese).
Support
Need help, found a bug, or have a directory-review question?
Re:port Flow (privacy & support): https://lp.re-port-flow.com
GitHub Issues: https://github.com/re-port-flow/reportflow-mcp/issues
License
MIT — see LICENSE.
Links
Re:port Flow: https://re-port-flow.com
Privacy & Support: https://lp.re-port-flow.com
Hugging Face: https://huggingface.co/reportflow
Agent guide: agents.md
Examples: examples/
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. Dates show when Glama detected each change.
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
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.
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