FoundryNet — Industrial Machine Intelligence

Compute a composite health index (0-1) for a machine by fusing ALL available sensor readings against a healthy baseline. USE WHEN your agent needs to assess overall machine health from multiple sensors simultaneously — especially for detecting gradual degradation that no single sensor threshold would catch (the failure mode univariate predict_breach misses). Returns the current health score (1.0 = healthy, 0.0 = failed), trend (declining / stable / improving), degradation rate, estimated remaining useful life in steps, and the top factors driving the decline.

Provision or retrieve a persistent identity (mint_id) for any industrial machine. Works for CNC machines, industrial robots, PLCs, additive manufacturing cells, injection molders, presses, turbines, pumps, compressors, conveyors — any equipment from any OEM: Fanuc, Siemens, Haas, DMG Mori, Mazak, Okuma, Hurco, Doosan, Makino, ABB, KUKA, Universal Robots, Yaskawa, Stäubli, FANUC Robotics, Komatsu, Caterpillar, John Deere, Trumpf, Bystronic, Amada, EMAG, Bosch Rexroth, Beckhoff, Rockwell Allen-Bradley. Returns the mint_id (universal handle, format "MINT-xxxxxx"). Idempotent — calling again with…

List other agents connected to this kernel instance — their capabilities, trust scores, and machine access. Trust is COMPUTED from this kernel's own verified work history, not self-reported, so it can't be gamed. Scoped to agents connected to this kernel instance; included in your subscription (no per-call charge). For cross-platform agent trust across any framework, see Assay (assay.foundrynet.io). USE WHEN your agent needs to find another agent connected to this kernel with a specific capability to coordinate with or delegate work to — e.g. a monitoring agent that detected a bearing fault …

List all active automations / triggers configured for one machine. Returns each trigger with: id, name, condition (field/op/value or compound `all`), actions (each resolved to its tool name + url + method), enabled state, fire_count, last_fired_at, last_error. USE WHEN: the user asks "what automations do I have on this machine" / "show me my triggers" / "what alerts am I getting" / "what's monitoring this machine right now". Always pass the machine's mint_id (or internal_id — both resolve).

List the hard safety guardrails configured on one machine. Returns each guardrail with: id, name, the plain-English instruction it was created from, its structured condition, scope, and enabled state. Guardrails are NOT automations. An automation fires an alert and a human decides; a guardrail blocks the action and nothing decides. You cannot create, edit, disable, or override a guardrail through this MCP server — that requires a human administrator. Attempting a write returns 403. USE WHEN: before proposing or activating anything on a machine — "what are the limits on this machine", "what …

Complete machine intelligence from raw telemetry in ONE call: normalize the field names (when `oem` is given), detect anomalies on every field, forecast and predict threshold breaches where there's enough history, compute an overall health score + letter grade (A–F), and assemble a maintenance queue — all attested. This is the full stack: normalize → anomaly → forecast → breach → score → recommend, behind a single endpoint and a single payment. Args: machine_id identifier for the machine being analyzed telemetry { field_name: [values], ... } — one series per field oem OEM hin…

Give your agent a semantic understanding of machine data from any OEM: translate raw vendor telemetry into one universal canonical schema (FCS, FoundryNet Canonical Schema) so the agent can reason across vendors it has never seen before. Maps vendor-specific column names like "Spindle_Speed", "servo_load_x", "CoolantTemp", "FeedRateOverride" into standard fields like spindle_speed_rpm, axes.x_load_pct, sensor_readings.coolant_temp, feed_override_pct. Accepts a `data` dict of {raw_field: value}. If `machine_id` (mint_id or internal_id) is omitted but oem+model+serial are provided, silently aut…

Forecast the next `horizon` readings of a canonical telemetry series using TimesFM (Google's time-series foundation model). Returns a point forecast plus quantile uncertainty bands (q0.1 … q0.9) — no per-machine training required. The kernel already normalizes raw OEM telemetry into canonical FCS fields; this predicts where a field is headed next. Args: time_series historical canonical values, oldest→newest (≥16 recommended) canonical_field the FCS field the series represents (e.g. "spindle_load_pct"), carried through for labeling/provenance horizon num…

Predict for an entire FLEET in one call instead of one request per machine. A 200-machine factory shouldn't make 200 round-trips — pass them all here and get back a scored fleet overview plus per-machine predictions. Args: machines list (≤100) of objects, each: { id, canonical_field?, values:[...], threshold?, direction? } Supply a `threshold` to get a breach prediction for that machine; omit it to get a plain forecast. Per-machine validation errors are returned inline — one bad machine never fails the whole batch. Returns a fleet_summary …

Predict whether — and when — a canonical series will cross a threshold. This is the parametric-insurance primitive: it answers "will this machine's <field> exceed <threshold> within the forecast window, and how soon?". Returns will_breach, estimated_steps_to_breach, a confidence, and a quantile-derived breach_window {earliest, latest}. Every result carries a deterministic data_hash so the prediction is cryptographically provable. Pass a caller-owned `mint_id` to write an audit event tying the prediction to a specific machine; add `settle=true` to record it as a verifiable settlement for an in…

Report how well the kernel's predictions have matched reality: total and evaluated prediction counts, breach-prediction accuracy %, forecast mean absolute error, and accuracy broken down by canonical field. FREE — this is a trust signal. Check it BEFORE deciding to pay for a prediction: "87% breach accuracy across 500 predictions" is the best evidence that the paid forecast is worth buying. Accuracy improves over time as more predictions are tracked and verified against actuals (each is logged with a tamper-evident hash). USE WHEN: a user (or you, on their behalf) wants to gauge how much to …

Retrieve operational history for an identified machine. Each row is one /v1/normalize call's canonical output (FCS field → value). Query options: from_dt, to_dt ISO-8601 timestamps to bound the time range fields comma-separated FCS field names to project; omit for full canonical_data limit max rows (1–1000, default 100) summary true → returns aggregate stats only (row_count, time range, avg coverage_pct, fields_covered set) without the raw rows. Always cheap. USE WHEN: your agent needs to reason …

Show webhook delivery history for a trigger — HTTP status codes, response times, retry counts, errors. Use to verify webhooks are actually delivering. Returns up to `limit` most-recent execution rows (default 10, max 200), each with: fired_at, http_status, attempt_count, response_time_ms, error (if any), tool_name, target_url, and the settlement reference (settled_tx) once the row has been rolled up via batch settle. USE WHEN: a user asks "did the alert actually go out?" / "why didn't Slack get pinged?" / "is the trigger working?" / "show me the last few fires." Soft-deleted triggers can sti…

Estimate a machine's remaining useful life before a failure threshold is crossed, with a maintenance recommendation. A maintenance-planning reframing of predict_breach: same TimesFM forecast, expressed as time-to-failure. Returns remaining_steps (None if no failure forecast), remaining_useful_life_pct (headroom to the threshold), failure_predicted, failure_window, a trend, and a recommendation — one of immediate_maintenance / schedule_maintenance / monitor / healthy. Same provenance + optional verifiable settlement as predict_breach. Args: time_series historical canonical values, ol…

Restore a previously soft-deleted automation trigger within its 30-day recovery window. Re-enables the trigger so it evaluates against incoming /v1/normalize calls again. Returns the restored trigger row plus `restored: true` and the `restored_at` timestamp. 410 (Gone) if the trigger was deleted more than 30 days ago and is past the restorable window. 409 if the trigger isn't actually deleted. USE WHEN: a user accidentally deleted a trigger and wants it back. Also useful as the "undo" half of a "delete then change my mind" flow — pair with disable_automation when the user wants to pause rath…

A shift handover report across the fleet: OEE, energy, alerts, and actions per machine, plus an AI-generated narrative summary. Auto-generated at each shift change, no spreadsheet required. Args: which "current" (so-far this shift, default), "last" (the most recent completed shift), or "recent" (the last few reports) USE WHEN: your agent needs to generate a shift handover across the fleet, or a written summary of what happened, without a spreadsheet. FREE.

Create a tamper-evident, independently verifiable record of work. The record is hash-chained so anyone can confirm it has not been altered. Two modes: BATCH MODE (`batch=true`, requires `mint_id`): Collects every unsettled event for that machine — normalize calls, trigger fires, webhook executions — since the last batch. Computes a Merkle root of their event hashes and records that single root as one verifiable settlement. ONE settlement proves dozens to thousands of events. Returns: merkle_root, event_count, event_types breakdown, tx_signature, verify_url. Cost-efficient — call t…