io.github.lesofi/handinloop-mcp-server

Fetch the validated extraction result for a completed job. Only call once handinloop_get_job_status reports status 'completed' (calling earlier returns an error with the remaining time). Returns: { job_id, task_type, status, fields: [{ name, value, confidence (0..1), human_verified }], line_items?: [...], // for invoice-like documents audit_trail: [{ at, actor: system|ai|human, action }] } Fields with human_verified=true were reviewed or corrected by a trained human — treat confidence 1.0 as authoritative. The audit_trail documents every step for compliance purposes; preserve it when storing results.

Check the status of a previously submitted extraction job. Statuses: 'queued' (waiting for a worker) -> 'in_review' (human processing) -> 'completed' (result ready) | 'rejected' (invalid document). Use estimated_seconds_remaining to pick a sensible polling interval; for scheduled routines, checking on the next run is usually enough. Args: - job_id: id returned by handinloop_submit_extraction_job Returns: { job_id, task_type, status, submitted_at, finished_at?, estimated_seconds_remaining? }

List the document extraction task types the HandInLoop human-in-the-loop platform supports. Call this first to discover valid 'task_type' values for handinloop_submit_extraction_job, and to see which fields each task type returns. Returns: { task_types: [{ id, name, description, output_fields[] }] }

Ask HandInLoop to propose the fields worth extracting from a document — the "what should I extract?" assist. Given a document (and an optional goal), the model suggests a field set you can then pass as 'fields' to handinloop_submit_extraction_job for a custom, human-verified extraction. Synchronous: returns immediately, no job, no human review. Args: - document_url OR document_base64: the document (PDF/image). Exactly one required. - file_name (optional) - goal (optional): focus, e.g. "accounts payable" Returns: { fields: [{ name, description }] }

Save a named custom field set as a reusable task type (#103 Phase B). Once saved, submit against it by passing its returned id as 'task_type' to handinloop_submit_extraction_job — no need to re-send the fields each time. Saved types also appear in handinloop_list_task_types. Saving an existing id updates it. Args: - fields: [{ name, description? }] — the field set (required, at least one). - name (optional): human name; the id is derived from it if 'id' is omitted. - id (optional): stable handle (lowercase letter then letters/digits/underscores). Must not collide with a built-in. - description (optional). Returns the saved TaskType { id, name, description, output_fields }.

Submit a document to HandInLoop for extraction with human-in-the-loop validation. AI extracts the document first; low-confidence fields are routed to a trained human reviewer. The result is validated data with per-field confidence and a complete audit trail. ASYNC: returns immediately with a job_id and status 'queued'. Human turnaround is minutes to hours in production — poll handinloop_get_job_status (respect estimated_seconds_remaining), then call handinloop_get_job_result once status is 'completed'. Ideal for scheduled/recurring workflows. Args: - task_type: one of the ids from handinloop_list_task_types (e.g. "invoice"), OR - fields: for CUSTOM extraction, describe the fields to extract yourself — [{ name, description? }]. Works on ANY document, not just the built-in task types. Provide task_type OR fields. - document_url OR document_base64: the document (PDF/image). Exactly one required. - file_name (optional): original filename for routing and audit - instructions (optional): note for the human reviewer Returns: { job_id, task_type, status, submitted_at, estimated_seconds_remaining }