obsify

Let an AI assistant work on sensitive files without their raw values ever entering the model's context.

obsify is a local, deterministic MCP server. The frontier model reasons over shape — schemas, synthetic twins, masked feedback — while deterministic local code touches the substance and returns only masked, aggregated results. No LLM calls, no network at runtime: detection is regex + checksums + dictionaries + Presidio's local NER.

It ships with Australian entity support (ABN / ACN / TFN, checksum-validated), credential/secret detection (cloud keys, API tokens, private keys, DB connection strings), and a label-driven routing layer that makes "when should the assistant avoid raw data" a deterministic, enforced decision rather than a judgement call.

Honest scope: run_on_real executes model-written code in a best-effort local sandbox and masks its output best-effort. It is not a jail. Read SECURITY.md before pointing it at anything you cannot afford to leak. Return aggregates.

Why

Feeding confidential documents to a hosted LLM means the substance leaves your perimeter. The usual answers are "don't use the LLM" or "trust the provider." obsify takes a third path — compute-to-data: bring the code to the data, not the data to the model.

• The model sees the schema of a spreadsheet, not its rows.
• The model develops against a synthetic twin (faked values, real structure).
• The model's analysis code runs locally; only masked, aggregated output returns.

The frontier model's reasoning is preserved. Only its eyes on raw values are removed.

Tools

Supported documents: PDF (text + tables; complex-table fallback via obsify[tables]), Excel .xlsx/.xlsm, and Word .docx (paragraphs + tables). Unreadable or unsupported files are surfaced as explicit notes/blind spots, never silently dropped. (No OCR yet — scanned/image pages are flagged as low-coverage, not transcribed.)

Known-entity masking (optional). Supply a local .obsify.entities list of names to hide; scan_pii / redact_text deterministically catch them — and the suffix/abbreviation variants NER misses (BRIGHTWATER HLDGS P/L for Brightwater Holdings Pty Ltd) — as KNOWN_ENTITY. The list stays local and never enters the model's context. See docs/known_entities.md.

Demo

Poke at all five tools live against synthetic data with the official MCP Inspector:

python -m obsify.make_corpus --out ./corpus_demo
npx @modelcontextprotocol/inspector obsify-mcp

Call scan_pii on ./corpus_demo/ledger.xlsx and confirm it returns types / counts / locations only — never values. See [`docs/ve

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