The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the JustFill PDF Form Automation listing page.
Let AI agents (Claude, ChatGPT, n8n — any MCP client) detect, review and fill PDF form fields through justfill.app.
If your MCP client supports remote servers with OAuth, use this server URL:
Connect from your client, sign in to JustFill, and review the access request. You do not need to install the Python package or copy an API key for this path. Access can be revoked in JustFill under Account → API Keys.
Connection guide. The client is MIT-licensed; PDF processing uses your JustFill account's allowance. For a first check, use a blank form and fictional values, and review the filled preview before exporting. Save the reviewed layout to reuse it with new data when the same form comes up again.
If the source data is already in a spreadsheet and you need one filled copy of the same existing PDF per row, an MCP client is optional. The guided browser workflow imports XLSX or CSV, maps columns to reviewed PDF fields, previews each record, and exports the approved PDFs in a ZIP.
Try the five-row PDF mail merge sample — no card or sales call.
Start with the deterministic workflow JSON in this repository. It collects a PDF and JSON payload, reuses the reviewed field names saved for that exact form, fills the original layout and returns a temporary download link.
The corrected workflow is also available in the n8n template library. The workflow is free to download; running it uses a JustFill account and its PDF-processing allowance. It uses built-in nodes, not the separate JustFill community node.
The repository also includes the exact deterministic workflow JSON, synthetic test PDF, production evidence and a separate two-pass vision workflow for an unfamiliar form. Both call the hosted MCP endpoint with standard HTTP Request nodes and can be inspected before adding credentials.
Inspect the source workflows and evidence, or follow the step-by-step n8n setup.
An operations team can keep the supplier's required intake PDF unchanged, save its reviewed field layout once, and let n8n map approved vendor data from a webhook or CRM record into that exact form. The workflow returns a temporary filled-PDF link that can be reviewed before it is uploaded to Drive, attached to a draft email, or written back to the vendor record. The repository's synthetic supplier-intake PDF exercises this exact path without customer data.
Install the same reviewed MCP tools plus the included PDF workflow guidance:
The extension manifest lives at the repository root and uses the published
justfill-mcp package. Gemini CLI asks for normal third-party extension consent
before enabling it.
| Source | Confidence | What it means |
|---|---|---|
| Saved template | 1.0 | This exact PDF was filled before; geometry is human/agent-verified. No ML runs at all. |
| AcroForm | 1.0 | The PDF has embedded form fields — read from the file, filled natively. |
| ML detection | 0.0–0.95 | An honest draft. Review it visually (render_preview), fix it, then save_template to lock it in. |
ML confidence is calibrated: the detector's raw scores are not
probabilities (its server-side filter accepts boxes from raw ~0.02 and
auto-accepts at raw 0.15), so they are mapped onto 0–1 to mean what you'd
expect — ≥0.75 "detector is sure", 0.4–0.75 "probably right, glance at the
preview", <0.4 "borderline accept, verify". The raw detector score is kept
on each field as raw_score.
The correction loop (render_preview → add/update/remove_field) exists
precisely because ML detection has false positives and negatives. A false
positive costs nothing (leave it unfilled or remove it); a false negative is
visible on the preview and fixable with one add_field call. Once reviewed,
save_template makes every future fill of that form deterministic.
Authorize once (opens the browser, one click while logged in to justfill.app):
Then the config needs no credentials at all:
For a zero-install configuration, use uvx directly:
Alternatives, in the order the server checks them:
JUSTFILL_API_KEY env — create a key at justfill.app → Account → API Keys
and put "env": {"JUSTFILL_API_KEY": "jf_live_…"} in the config.justfill-mcp login (~/.config/justfill/credentials.json).JUSTFILL_EMAIL + JUSTFILL_PASSWORD — legacy fallback; an API key is
better (no password in config files, revocable per client, never expires
mid-session).open_pdf(path, min_confidence=0.0, max_pages=10, force_detect=False) —
template → AcroForm → ML resolution order. Accepts scanned images too
(jpg/png/tiff → converted to PDF, deterministically, so templates still
match). force_detect=True ignores a saved template and re-runs ML.render_preview(page_index) — page image with labeled field boxes (blue = deterministic, green/orange/red = ML confidence)render_filled_preview(values, page_index) — the same page with your values
drawn in place (checkboxes get an X). Costs no fills — check before you fill.list_fields(page_index?)add_field(x, y, w, h, name, page_index, field_type, align?, vertical_align?) — coords in % of page, top-left originupdate_field(field_id, …) / remove_field(field_id)update_fields([{field_id, …}, …]) / remove_fields([ids]) — batch versionsprune_fields(field_type?, confidence_below?, width_below?, height_below?, page_index?, exclude_ids?) —
bulk-delete detection noise in one call (criteria AND-ed, removed ids returned)fill_pdf(values, output_path, flatten=True) — values = {field_id: text};
responds with warnings for values that will be shrunk/truncated to fitsave_template(name) — persist the reviewed layout for deterministic repeat fillslist_templates()Text alignment: align = left|center|right, vertical_align =
top|middle|bottom — set per field (e.g. right for RTL forms, center for
boxed digits). Persisted in templates.
fill_pdf reports whether the output is clean or watermarked.