The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Sheetrender MCP listing page.
MCP server for SheetRender. It lets your AI assistant render PDFs from HTML templates and spreadsheet data.
Get an API key from sheetrender.com under Settings → API keys, then add this to your MCP client config:
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.
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:
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:
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.
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:
render_pdfRenders one PDF from HTML you supply.
| Argument | Type | |
|---|---|---|
html | string | Required. A full HTML document. CSS has to be inline in a <style> tag; external stylesheets, fonts and scripts are not fetched. |
data | object | Optional. Keys become Jinja variables, so {"total": "42.00"} makes {{ total }} available in the HTML. |
page_settings | object | Optional, see below. |
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_templatesNo 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_templateRenders one PDF from a template already saved in the account.
| Argument | Type | |
|---|---|---|
template_id | string | Required, from list_templates. |
data | object | Required. One row's values, as Jinja variables. |
page_settings | object | Optional. Omit it to keep the template's own saved page setup; passing it overrides that for this render. |
Same return as render_pdf.
create_datasetTurns 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.
| Argument | Type | |
|---|---|---|
template_id | string | Required, from list_templates. The dataset lands in that template's project. |
rows | array of objects | Required. One flat object per document. |
name | string | Optional label, used as the stored filename. |
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_datasetThe same thing from a file that already exists.
| Argument | Type | |
|---|---|---|
template_id | string | Required, from list_templates. |
file_path | string | Required. A .csv or .xlsx on the machine running this server — the user's machine, not SheetRender's. ~ is expanded. |
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_datasetsTakes 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_jobQueues 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.
| Argument | Type | |
|---|---|---|
template_id | string | Required, from list_templates. |
dataset_id | string | Required, from create_dataset, upload_dataset or list_datasets. Must be in the same project as the template. |
filename_template | string | Optional. Output naming pattern, e.g. invoice-{{ invoice_no }}. |
group_by | string | Optional. Column key to group rows by, giving one multi-page PDF per distinct value. |
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_jobTakes 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_documentTakes 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_settingsShared by both render tools. Every field is optional:
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.
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.