@sheetrender/mcp

MCP server for SheetRender. It lets your AI assistant render PDFs from HTML templates and spreadsheet data.

Setup

Get an API key from sheetrender.com under Settings → API keys, then add this to your MCP client config:

{
  "mcpServers": {
    "sheetrender": {
      "command": "npx",
      "args": ["-y", "@sheetrender/mcp"],
      "env": { "SHEETRENDER_API_KEY": "sr_live_..." }
    }
  }
}

SHEETRENDER_API_URL is also read, and defaults to https://sheetrender.com. Set it only if you're pointing at a self-hosted or staging instance.

Hosted endpoint

The same server runs at https://mcp.sheetrender.com/mcp over Streamable HTTP, so clients that can't spawn a local process can use it too. Nothing is installed; each request carries your API key:

Authorization: Bearer sr_live_...

Requests without that header get a 401. The key goes straight through to the SheetRender API for that one request and is never stored — the server keeps no sessions, so every request stands alone.

Where the key goes depends on the client:

• Claude Code: claude mcp add --transport http sheetrender https://mcp.sheetrender.com/mcp --header "Authorization: Bearer sr_live_..."

• Cursor, Windsurf, VS Code and other clients with an mcp.json:

  config.json

  Copy

  {
    "mcpServers": {
      "sheetrender": {
        "url": "https://mcp.sheetrender.com/mcp",
        "headers": { "Authorization": "Bearer sr_live_..." }
      }
    }
  }
  

• Claude API (the Messages API's MCP connector): add {"type": "url", "url": "https://mcp.sheetrender.com/mcp", "name": "sheetrender", "authorization_token": "sr_live_..."} to mcp_servers.

• claude.ai, Claude Desktop and ChatGPT custom connectors take a server URL and an OAuth client, not a static header. Until the endpoint speaks OAuth, use the stdio package above there — it's the same tools, with the key in env — or bridge with npx mcp-remote https://mcp.sheetrender.com/mcp --header "Authorization: Bearer sr_live_..." as the command.

Two differences from the stdio server follow from the process not running on your machine. Rendered PDFs come back inline as a base64 resource (up to 8 MB; larger ones are reported with their size and left for the web app) instead of as a temp-file path, and upload_dataset is not offered because there is no local file to read — send rows with create_dataset instead.

GET /healthz answers 200 without credentials. Request bodies are capped at 25 MB; anything larger is a 413.

Running it yourself

sheetrender-mcp-http is a second bin in the package. It reads PORT (default 8080), HOST (default 0.0.0.0), SHEETRENDER_API_URL, MAX_BODY_BYTES and IDLE_TIMEOUT_MS (default 60000), and logs one JSON line per request to stdout — method, path, status, duration, the JSON-RPC method and tool name, and a fingerprint of the key, never the key. The Dockerfile in this repo builds a non-root runtime image for it:

docker build -t sheetrender-mcp .
docker run --rm -p 8080:8080 sheetrender-mcp

Tools

render_pdf

Renders one PDF from HTML you supply.

Returns the path of the saved PDF and its size. Under 512 KB it's also attached inline as a base64 resource, so clients that display attachments show the document itself.

Two server limits apply. HTML over 2 MB is rejected, and that's measured both on what you send and on the document after data is substituted in, so a template that expands a long dataset can cross the line even when the markup you wrote doesn't. The other is the free plan, where every rendered PDF carries a "Made with SheetRender" footer. That applies to render_pdf and render_template alike. Paid plans don't get it.

list_templates

No arguments. Returns each saved template's name, id and last-updated date. Call it to turn a template name the user mentioned into the id the other tools want.

render_template

Renders one PDF from a template already saved in the account.

Same return as render_pdf.

create_dataset

Turns JSON rows into a dataset a batch job can render. This is the usual way to start a batch: assemble the rows, send them, get back a dataset_id.

The header is the union of every row's keys in first-seen order, so rows don't have to agree on their keys — a missing one is a blank cell rather than a shifted row. Values have to be scalars: strings, numbers, booleans or null. Nested objects and arrays are rejected, and so are NaN, Infinity and whole numbers past 2^53 (send those as strings to keep them exact). The caps are 50,000 rows and 500,000 cells per call.

Returns the dataset id, row count and, for each column, the sanitized key. That key is what template placeholders, filename_template and group_by address, and it's often not the header verbatim — Invoice No becomes invoice_no. Read it off this result instead of guessing.

Creating a dataset is free; only rendering counts against the plan.

upload_dataset

The same thing from a file that already exists.

The first row has to be the header. Files over 20 MB, the wrong extension and empty files are refused locally, before anything is uploaded. Same return as create_dataset.

list_datasets

Takes template_id and lists every dataset in that template's project, newest first, with ids, row counts and column keys. Use it to find data the user already loaded, or to read a dataset's column keys before writing a filename_template or picking group_by.

create_batch_job

Queues a background job that renders one PDF per row of a dataset. The whole loop runs from here — list_templates → create_dataset or upload_dataset → create_batch_job → get_job → get_document.

Returns the job id to poll with get_job. Worth knowing: filename_template and group_by are persisted to the template and the project respectively, so they change the defaults for later runs too.

If the server predates the public batch endpoint, the tool reports that batch jobs are unavailable rather than failing obscurely. The three dataset tools do the same for a server that predates the dataset endpoints.

get_job

Takes job_id. Returns the status, rows done and failed, and the id and filename of every rendered document. That document list stays empty while the job is queued, retry_queued or running, and fills in once the job reaches succeeded, partial, failed or cancelled. Those document ids are what get_document takes.

get_document

Takes document_id and downloads that single rendered PDF. The ids come from get_job on a finished batch, and there's no other way to get one. Same return as render_pdf: path, size, and an inline blob under 512 KB.

If you want a whole batch, the merged PDF and ZIP in the web app beat fetching each document in turn.

page_settings

Shared by both render tools. Every field is optional:

{
  "page_size": "a4",
  "orientation": "portrait",
  "margins": { "top": 15, "right": 15, "bottom": 15, "left": 15 }
}

page_size is lowercase: a3, a4, a5, letter, legal or tabloid. Margins are plain numbers in millimetres, not CSS lengths.

Rendered PDFs are written to the system temp directory. API errors like a bad key, a missing template or a rate limit come back as tool errors carrying the server's own message.

The public API allows 120 requests per minute per API key. Past that it returns 429, and the tool reports that you're rate limited and should retry shortly. Batch job creation is metered separately and more tightly.

Development

You don't need node or npm on the host: scripts/dev.sh install, then scripts/dev.sh deno task build and scripts/dev.sh deno task test. scripts/dev.sh node dist/http.js runs the HTTP server on the host network.

MIT licensed.