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Last probed Nov 3, 2026 · mcp.foundrynet.io
32tools discovered
For AI agents — MCPBundles Directory API
For MCP client apps (human connect):
MCP connect URL: https://mcp.foundrynet.io/mcp
Skill page: https://www.mcpbundles.com/skills/foundrynet-mcp-f3a1bcf6b4
Explore capabilities (32)
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Predict3
List2
Query2
Other capabilities18
Identify Machine
other
identify_machine
Provision or retrieve a persistent machine_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.
R
Description may be truncated on the public preview.
Identify Machine — Provision or retrieve a persistent machine_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.
R
Normalize Telemetry — 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}.
Query Machine History — 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_covere
Create Automation — Let your agent wire machine telemetry to any business system in plain
English — ERP, CMMS, MES, Slack, Teams, email, Zapier, n8n — via webhooks
already registered as tools on the Forge service. The agent describes the
condition and the action; Forge parses it into a structured trigger.
Examples of `instruction`:
"Alert maintenance Slack when spindle load exceeds 90 percent."
"Create a Fiix work order when coolant temperature stays above 35°C
for five minutes."
"Notify the supervisor wh
Activate Automation — Activate a parsed automation trigger on a machine. Call this AFTER
create_automation returns a parsed_trigger and the user explicitly
confirms they want to arm it.
Creates a live trigger that monitors the machine's normalized telemetry
and fires the listed actions when the condition matches. Each action
references a registered tool by tool_id; on fire, the tool's webhook
is POSTed with {{variable}} interpolation against the canonical data
context (mint_id, oem, model, serial, site, field, value
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Foundrynet is an MCP server at mcp.foundrynet.io. MCPBundles discovered 32 tools on the live endpoint.
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List Automations — 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 Guardrails — 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:
Check Guardrail — Test whether a proposed action would be blocked, WITHOUT attempting it.
`proposed` is a dict of canonical field -> value you are considering, e.g.
{"spindle_speed_rpm": 13000}. Returns:
{"would_block": true, "guardrail": "Spindle overspeed protection",
"reason": "spindle_speed_rpm 13000 > limit 12000", "violations": [...]}
or {"would_block": false, ...} when nothing is violated.
This is a dry run: it writes nothing and is not recorded as a blocked
attempt. Call it BEFORE acting so you can
Disable Automation — Pause an automation trigger without deleting it. The trigger stops
evaluating against incoming /v1/normalize calls but its configuration
(condition, actions, history) is preserved. Re-enable later by
PATCHing /v1/triggers/{id} with `{"enabled": true}` (or by asking
the user to confirm and creating a follow-up tool for resume).
USE WHEN: the user wants to TEMPORARILY stop an automation — e.g.
"pause the high-spindle alert during planned maintenance," "stop
that alarm for now, I'll re-enable it t
Delete Automation — Soft-deletes the trigger (recoverable for 30 days via
restore_automation). The trigger immediately stops evaluating against
/v1/normalize calls and is hidden from list_automations, but the row
persists with deleted_at set so an accidental delete can be undone.
Use restore_automation to undo. For permanent deletion, the API
supports ?permanent=true.
Past forge_trigger_executions rows for this trigger remain in either
case (audit trail).
USE WHEN: the user wants to remove an automation they no l
Query Webhook History — 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 ge
Restore Automation — 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
Verify Record — 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:
Fire Sandbox — Demo the full Forge watch→fire→settle loop against a built-in sandbox
endpoint. Free tier; no machine onboarding required.
The MCP server POSTs `{message, condition: condition_text, ts}` to its
own /sandbox/echo route — a real HTTP round-trip with a real response
body — then hashes the response and records a tamper-evident, verifiable
work record of it. Returns the echo body, the tx_signature, and a verify_url.
USE WHEN: a developer is evaluating Forge and wants to feel the full
loop (a webhoo
Correct Mapping — Teach Forge the RIGHT canonical field for a source column that
normalize_telemetry mapped wrong (or abstained on). Each correction is
recorded as a corpus-improvement signal the retrainer uses to fix the
mapping for everyone — so every agent interaction makes normalization better.
USE WHEN: you or the user can see normalize_telemetry returned the wrong
canonical for a field (e.g. it mapped an oil-pressure column to a tire-
pressure field), or it abstained on a field whose meaning you know.
- s
Get Coverage — Ask Forge what it can normalize BEFORE you try: the recognized OEM
verticals (CNC / robot / vehicle / AMR), the canonical-field families, and
the field list per family. Optionally pass an `oem` to see which vertical it
resolves to and whether the cross-vertical gate will engage.
USE WHEN: starting a new integration, or deciding whether to call
normalize_telemetry — confirm the machine's OEM and your fields are in
coverage. Unknown OEMs still normalize (the gate just disables itself), so
absence
Predict — 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. "s
Predict Breach — 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 p
Remaining Life — 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
Predict Batch — 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.
Fleet Health — Roll a fleet of machine predictions up into a single health dashboard: a
fleet health score, a critical/elevated/moderate/healthy risk distribution,
per-canonical-field risk rollups, and a maintenance priority queue with a
plain-English recommendation.
Args:
machines same shape as predict_batch — list (≤100) of
{ id, canonical_field?, values:[...], threshold?, direction? }.
Machines with a `threshold` are bucketed by steps-to-breach
(critical <6, elevated
Detect Anomalies — Flag anomalies in a time series WITHOUT running a full forecast — z-score +
IQR outlier detection plus trend and rate-of-change (accelerating/steady/
decelerating). No TimesFM inference, so it's faster and cheaper than predict
and works on short series (≥4 points). Cross-references the canonical field's
known/derived normal range when one is available.
Args:
values the series to scan (≥4 points)
canonical_field FCS field the series represents (enables normal-range context)
sens
Machine Intelligence — 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.
Prediction Accuracy — 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
Calculate Oee — Overall Equipment Effectiveness (OEE = Availability × Performance ×
Quality) for one machine over a period, computed from telemetry the kernel
already collects. Returns the three-factor breakdown, a letter grade, and an
honest `available:false` with a reason when there isn't enough data.
Args:
machine_id the machine's id (e.g. "DEMO-FANUC-01")
period "shift" (default), "day", or "week"
USE WHEN: your agent needs to report how a machine is performing, locate
where the losses are, or p
Showing the first 25 tools — full list and schemas: Directory API.
MCPBundles probed 32 tools on the live server. The tool list on this page reflects what was discovered at the last refresh — connect your client to see the full set available to your session.
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