JustFill MCP Server

Let AI agents (Claude, ChatGPT, n8n — any MCP client) detect, review and fill PDF form fields through justfill.app.

<!-- mcp-name: io.github.mrmaciej1/justfill -->

Why agents can trust 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.

Setup

uv tool install ./mcp-server        # or: pip install ./mcp-server

Authorize once (opens the browser, one click while logged in to justfill.app):

justfill-mcp login

Then the config needs no credentials at all:

{
  "mcpServers": {
    "justfill": { "command": "justfill-mcp" }
  }
}

Alternatives, in the order the server checks them:

1. JUSTFILL_API_KEY env — create a key at justfill.app → Account → API Keys and put "env": {"JUSTFILL_API_KEY": "jf_live_…"} in the config.
2. The key saved by justfill-mcp login (~/.config/justfill/credentials.json).
3. JUSTFILL_EMAIL + JUSTFILL_PASSWORD — legacy fallback; an API key is better (no password in config files, revocable per client, never expires mid-session).

Tools

• 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 origin
• update_field(field_id, …) / remove_field(field_id)
• update_fields([{field_id, …}, …]) / remove_fields([ids]) — batch versions
• prune_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 fit
• save_template(name) — persist the reviewed layout for deterministic repeat fills
• list_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.

Example agent flow

open_pdf("~/forms/w-9.pdf")            → acroform, 27 fields, confidence 1.0
fill_pdf({"f1": "Jane Doe", …}, "~/out/w-9-filled.pdf")

open_pdf("~/forms/scan.jpg")           → converted to PDF; ml, 34 fields
render_preview(0)                      → agent sees noise + one missed line
prune_fields(field_type="cell", width_below=3)   → 16 removed in one call
add_field(x=18, y=62.5, w=40, h=3, name="Phone")
render_filled_preview({…})             → values sit right, no overflow
fill_pdf({…}, "~/out/filled.pdf")
save_template("Client intake form")    → next time: deterministic

Notes

• Auth is a regular justfill.app account; tokens auto-refresh on expiry.
• Pricing follows the same rules as the web app: template and AcroForm resolution never cost anything; ML detection is free on the free plan (daily caps apply) and uses per-page credits on paid plans; downloads consume the account's fill allowance/credits.
• One PDF open at a time per server session (by design — keeps ids stable).
• This repository mirrors released versions of the MCP client (development happens in a private monorepo alongside the justfill.app backend). Bug reports and feature requests are very welcome in the issue tracker here.