ReportFlow MCP: AI-Powered Report Generation Tool

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Reportflow MCP

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update time : 2026-08-14

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Installation

Copy the following command to your Client for configuration

{
  "mcpServers": {
    "reportflow": {
      "command": "npx",
      "args": ["-y", "reportflow-mcp"]
    }
  }
}

Note: Your key is sensitive information, do not share it with anyone.

🚀 Re:port Flow MCP

Official display name: Re:port Flow MCP · Package identifier: reportflow-mcp · Legacy aliases: ReportFlow MCP Server, ReportFlow

Turn 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.

📖 Overview

An MCP (Model Context Protocol) server that transforms your Re:port Flow templates into production-ready PDF reports. Generate invoices, contracts, statements, and any other design you've built — directly from Claude or any MCP-compatible AI agent, using natural language.

✨ Features

📝 Natural-language generation — Create PDFs from requests like "create an invoice for Acme Corp totalling $300"
🔗 Design exposure as MCP Resources — Your Re:port Flow designs and their parameter schemas are surfaced directly to the AI
📦 Bulk generation with ZIP packaging — Generate many PDFs at once and download them as a single ZIP
💾 Smart workspace output — Save results 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/mcp

In 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)

Use the same JSON in .vscode/mcp.json.

🧩 Requirements

Mode	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 is required 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 via server/discover; no session header, and 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) — classic initialize handshake, 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.

1️⃣ 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.

2️⃣ 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_sync writes a single ZIP to your workspace.
Remote: generate_pdfs_async runs the batch and returns a request id plus a download URL.

3️⃣ 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_params to turn the brief into a valid params object before generating.

4️⃣ 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
`/generate_pdf`	Step-by-step recipe for a single PDF
`/generate_pdfs`	Recipe for batch PDF generation
`/reportflow_help`	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

📚 Reference

🛠️ Tools (called by the AI)

Tool	Purpose
`authenticate`	First-time / re-authentication
`list_templates`	List available designs
`get_design_parameters`	Fetch the parameter schema for a design
`generate_pdf_sync` / `_async`	Generate one PDF (sync returns path; async returns request ID)
`generate_pdfs_sync` / `_async`	Generate many PDFs (returns a ZIP)
`download_file` / `download_zip`	Download artifacts produced by async tools
`suggest_params`	Translate a natural-language brief into a `params` JSON via MCP Sampling (requires a Sampling-capable client)
`search` / `fetch`	ChatGPT connector convention tools (single string argument), closed-world (`openWorldHint: false`) — they only read your own workspace's internal template catalog, never the web. `search` resolves templates by name; `fetch` returns a template's parameter schema by id. Thin wrappers over `list_templates` / `get_design_parameters` so ChatGPT (incl. Plus/Pro without Developer Mode) can discover and inspect templates.
`search_gallery_templates`	Search the public template gallery (no auth needed) by keyword/category. Returns candidate templates that are not yet in your workspace — their `slug` cannot be used for PDF generation until copied.
`get_gallery_template`	Fetch full details of one public gallery template by `slug` (no auth needed)
`copy_gallery_template`	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 `designId` + `version` ready for `get_design_parameters` / `generate_pdf_sync`. Each call creates a new design — it never reuses a previous copy.

📎 Resources (attachable as AI context)

URI	Contents
`reportflow://designs`	List of available designs
`reportflow://designs/{designId}/parameters`	Parameter schema for one design
`reportflow://errors`	Catalog of error messages from the Content Service
`reportflow://server-info`	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 `re-authentication required`	Ask the AI: "re-authenticate with Re:port Flow"
`npx` cannot find the package	`npm cache clean --force` then retry
No keychain available on Linux	Falls back automatically to a chmod-0600 file under `$XDG_STATE_HOME/reportflow-mcp/`
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).