How do I install @sheetrender/mcp?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does @sheetrender/mcp need an API key?

No required environment variable is listed on this record.

What transport does @sheetrender/mcp use?

@sheetrender/mcp runs locally over stdio.

When was @sheetrender/mcp last checked?

Last checked 2026-10-28. MCP server for SheetRender: render PDFs from HTML templates and spreadsheet data via the SheetRender API.

เซิร์ฟเวอร์ MCP @sheetrender/mcp

@sheetrender/mcp

MCP server for SheetRender: turn rows of a spreadsheet into one PDF each, such as certificates, letters, donation receipts and offer letters.

The quickest start needs no account and no install. Add the hosted server https://mcp.sheetrender.com/mcp as a connector in ChatGPT, Claude or any client that takes a Streamable HTTP URL, then paste or describe your rows. It fills SheetRender's built-in templates and returns a preview and a PDF per row (details).

With a SheetRender API key you get the full tool set: your own saved templates, HTML templates, datasets and batch jobs, either through the npm package below or the same hosted URL.

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 the smaller no-key tool set described below. 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:

{
  "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.

A key that merely starts with sr_ proves nothing, so the server waits for the SheetRender API to accept it. Until a call made with the key has succeeded (normally the first tool call, e.g. list_templates), its requests get the same bounds as requests without a key, described below: bodies up to 2 MB, at most 4 JSON-RPC messages per batch, and every message counted toward the per-network budgets. Once the API has accepted the key, the server remembers a hash of it (never the key) for an hour after its latest successful call, and its requests get the 25 MB body cap and batches of up to 20 messages. MCP clients send one message per request, so the batch caps only matter to hand-written clients. If the very first call is a body over 2 MB (a large create_dataset), it gets a 413 that says to make a small call first and retry. Every request, with a key or without, counts toward the server-wide in-flight cap.

One tool call's arguments may hold at most 600,000 values (array elements and object fields together) from an accepted key, enough for create_dataset's 500,000 cells, and 20,000 from a key not yet accepted. Anonymous calls are capped at a little over the largest real call.

Bodies over 2 MB also take one of a few server-wide large-body slots, 1 by default (LARGE_BODY_MAX_IN_FLIGHT). A slot is taken before reading when Content-Length declares such a body, or as soon as a streamed body passes 2 MB, and is held until the response ends. When all are taken, the request gets a 503 with Retry-After: 5 and the rest of its body is not read. One 25 MB body can take around 100 MB of heap while it is decoded, parsed and passed on, so raise this only with the heap (the hosted container runs a 192 MB heap).

Without a key (ChatGPT and Claude directory listings)

When the server runs with SHEETRENDER_DEMO_API_KEY set, a request that sends no Authorization header gets a different, smaller tool set instead of a 401. These tools fill SheetRender's built-in templates (certificate of completion, letter, donation receipt, job offer letter) from rows in the chat, through a dedicated rendering account:

list_document_templates: the templates, their fields (key, label, required, example, character and line limits).
render_documents: up to 25 rows per call, one PDF per row; returns a PNG preview and a PDF link per document (both expire after an hour), the rows that missed a required field, and how many of the month's 50 documents per user (in Claude, per conversation) remain. When a limit shared with other users is what cut or refused the call, it says the limit is shared instead of showing a count. A refused or cut call also says the rows can be taken to the website with a continue link. A value over its field's character or line limit is refused before the API is called, and the API's own reason for refusing rows (row number, field and rule, never the value) is passed on.
create_continue_link: up to 100 rows, stored for 7 days, and a link to the template's page on the website with those rows loaded. Called only when the user wants to continue there.

render_documents comes with an MCP Apps view, ui://sheetrender/documents.html (text/html;profile=mcp-app): a strip of previews with PDF links and a "Continue in SheetRender with these rows" button. Its script, src/widget/documents.ts, is bundled by esbuild into dist/widget/documents.js during deno task build and inlined into the page when the resource is read.

Calls are counted per user: ChatGPT's anonymised openai/subject, else the client IP. Any caller can send openai/subject, so it is believed only from OpenAI's published egress ranges (chatgpt-connectors.json, overridable with OPENAI_EGRESS_CIDRS); from anywhere else the call is counted by IP. The hosted server fetches that list at startup and every 24 hours, keeping the built-in copy (then the last good fetch) whenever a fetch fails or returns anything malformed or empty; it logs one line per refresh. It also logs, at most once an hour, how many calls sent an openai/subject from outside the list, so a stale list shows up.

The server allows 30 render or continue calls per subject or IP per hour (ANON_CALLS_PER_HOUR). Callers counted by IP also share a second bucket per IPv4 /24 or IPv6 /48, 300 calls an hour (ANON_NETWORK_CALLS_PER_HOUR), so a block of cheap addresses counts as one caller. All traffic from Claude's 160.79.104.0/21 network (Anthropic's published outbound range, which the Claude API's MCP connector also uses) shares a separate 3,000-call hourly bucket (CLAUDE_CALLS_PER_HOUR); each Claude conversation that sends its Mcp-Session-Id also gets the 30-call per-subject bucket inside it. Neither User-Agent text nor _meta can claim or leave these buckets. The SheetRender API still applies the monthly document volume against the hashed subject or IP. The subject and IP are sent to the API only as SHA-256 hashes, and the request log carries a fingerprint, the detected client (chatgpt, claude or other) and the row count, never the rows.

Anonymous HTTP bodies are capped at 2 MB, enough for 25 rows at every field's limit; a continue link's rows are capped at 256 KB. A private socket peer is treated as a proxy and only the final valid IP in X-Forwarded-For is trusted. Keep this listener behind Caddy, with no publicly exposed container port, as in the deployment's Compose topology.

Anonymous requests have three more bounds, so a small request cannot make the server generate a lot of output. A JSON-RPC batch may carry at most 4 messages (MCP dropped batching in protocol version 2025-06-18, and ChatGPT and Claude send one message per request); a larger one gets a 400 before anything in it runs. Messages other than tool calls (initialize, tools/list, resources/read, ...) count 600 an hour per IPv4 /24 or IPv6 /48 (ANON_RPC_PER_HOUR); traffic from OpenAI's and Claude's egress ranges is not counted there, since each of those addresses carries many users. And at most 64 requests, with a key or without, are answered at once (ANON_MAX_IN_FLIGHT), 8 per network outside those ranges (ANON_NETWORK_MAX_IN_FLIGHT); past either, the answer is a 503 or 429 with Retry-After: 5. Requests with an API key the SheetRender API has not yet accepted get all three bounds, with tool calls counted toward the 600 as well; once the key is accepted they count only toward the overall in-flight cap, and their batches may carry 20 messages.

A request carrying a SheetRender bearer key uses the API-key tools above. A malformed Authorization header is rejected. The stdio server never offers the anonymous tools.

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, a fingerprint of the key (never the key), and key_verified, whether the API had accepted that key yet.

For the anonymous tools, and the request bounds above (which also apply to keyed requests when no demo key is set), it also reads:

Variable
`SHEETRENDER_DEMO_API_KEY`	The dedicated rendering account's `sr_` key. Unset: no anonymous tools, keyless requests get a 401.
`MCP_PUBLIC_URL`	The URL users paste, byte for byte, e.g. `https://mcp.sheetrender.com/mcp`. Sets the view's sandbox origin (Claude hashes this exact string).
`OPENAI_APPS_CHALLENGE`	OpenAI's domain-verification token, served as plain text at `GET /.well-known/openai-apps-challenge` (404 when unset).
`ANON_CALLS_PER_HOUR`	Render and continue calls per subject or IP per hour, default 30.
`ANON_NETWORK_CALLS_PER_HOUR`	Render and continue calls per hour per IPv4 /24 or IPv6 /48, for callers counted by IP, default 300.
`CLAUDE_CALLS_PER_HOUR`	Shared render and continue calls per hour for all traffic from `160.79.104.0/21`, default 3000.
`ANON_RPC_PER_HOUR`	Anonymous messages other than tool calls, plus every message sent with a key the API has not yet accepted, per hour per IPv4 /24 or IPv6 /48, outside the OpenAI and Claude ranges, default 600.
`ANON_MAX_IN_FLIGHT`	Requests answered at once, all callers together (with a key or without), default 64.
`ANON_NETWORK_MAX_IN_FLIGHT`	Anonymous requests, and requests with a key the API has not yet accepted, answered at once per IPv4 /24 or IPv6 /48, outside the OpenAI and Claude ranges, default 8.
`LARGE_BODY_MAX_IN_FLIGHT`	Requests with a body over 2 MB handled at once, server-wide, default 1. Only keys the API has accepted can send such a body.
`OPENAI_EGRESS_CIDRS`	Comma- or space-separated CIDRs whose `openai/subject` is believed, replacing both the built-in copy and the live fetch of chatgpt-connectors.json; `none` believes no subject.

With SHEETRENDER_DEMO_API_KEY set, the server refuses to start if the view bundle (dist/widget/documents.js) is missing. 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

design_template: Design from exactly one of dataset_id, inline rows, data_base64 plus data_filename, or stdio-only data_path, and an example, brief or style. Data files must be CSV or XLSX, up to 10 MB. Waits up to 3 minutes and returns status, template details and an authenticated preview URL. Examples use example_base64 plus example_filename, or stdio-only example_path.
get_design: Poll a design_id for its status, template id, name, column mapping and preview URL.

`render_pdf`

Renders 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.

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. A "Made with SheetRender" footer is added when required by the rendering account's settings. That applies to render_pdf and render_template alike.

`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.

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_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.

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 does not consume document volume; only rendering does.

`upload_dataset`

The 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_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.

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_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.

@sheetrender/mcp

เพิ่มเซิร์ฟเวอร์นี้

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install @sheetrender/mcp?

Does @sheetrender/mcp need an API key?

What transport does @sheetrender/mcp use?

When was @sheetrender/mcp last checked?

Documentation

@sheetrender/mcp

Setup

Hosted endpoint

Without a key (ChatGPT and Claude directory listings)

Running it yourself

Tools

`render_pdf`

`list_templates`

`render_template`

`create_dataset`

`upload_dataset`

`list_datasets`

`create_batch_job`

`get_job`

`get_document`

`page_settings`

Development