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.

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)

Agent Skill

Agents that should follow a procedure (not just read the protocol) use skills/re-port-flow/SKILL.md. Copy that directory into Claude Code / Codex .agents/skills or a Cursor project skill folder. PDF generation always goes to https://mcp.re-port-flow.com/mcp. The Hugging Face Space MCP (org card) is gallery-only — add it from the Space MCP badge → Add to MCP tools, not as a substitute for the product endpoint.

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)

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 via server/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) — 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.

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_sync writes a single ZIP to your workspace.
• Remote: generate_pdfs_async runs 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_params to turn the brief into a valid params object 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

Where files are saved (local mode)

Output location is resolved in this order:

1. Explicit instruction from the user (e.g. "save to my Desktop")
2. The currently-open workspace root (Claude Code / Cursor / VS Code)
3. 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/mcp

• agents.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_template is not idempotent; passthrough values 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)

Resources (attachable as AI context)