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Add FoundryNet — industrial machine intelligence and automation via MCP #4111

@FoundryNet

Description

@FoundryNet

Server Name

FoundryNet

What does this server do?

FoundryNet lets AI agents talk to industrial machines. It normalizes
raw telemetry from any CNC, robot, or industrial equipment (Fanuc,
Siemens, Haas, DMG Mori, Komatsu, Mazak, ABB, KUKA, Universal Robots)
into a universal canonical schema, enables natural language automations
(alert on conditions, trigger webhooks to ERP/CMMS/MES/Slack), and
verifies everything on Solana mainnet.

Tools (11)

  • identify_machine — Provision persistent identity for any industrial machine
  • normalize_telemetry — Map raw OEM telemetry to universal canonical schema
  • query_machine_history — Retrieve operational history and health trends
  • create_automation — Parse natural language into automation triggers
  • activate_automation — Arm a parsed trigger after user confirmation
  • list_automations — Show all active triggers on a machine
  • disable_automation — Pause a trigger without deleting
  • delete_automation — Soft-delete with 30-day restore window
  • restore_automation — Recover a deleted trigger
  • query_webhook_history — Audit webhook delivery status and errors
  • verify_on_chain — Anchor data on Solana mainnet with Merkle proof

Transport

SSE

Endpoint

https://foundrynet-mcp-production.up.railway.app/sse

Source Code

https://github.com/FoundryNet (server source to be published)

Categories

Industrial, Manufacturing, IoT, Automation, CNC, Robotics, Telemetry

Why this server matters

There are currently zero MCP servers that handle cross-manufacturer
industrial data normalization or natural language factory automation.
Existing industrial MCP servers (Litmus, OPC UA, PolyMCP) handle
machine connectivity — getting data off PLCs. FoundryNet handles
machine intelligence — making that data universally understandable
and actionable across any business system.

Demo

Verified end-to-end in Claude Desktop: machine identification on
Solana mainnet, telemetry normalization at 100% field coverage,
natural language trigger creation with sustained thresholds,
webhook execution with HMAC signing, Merkle-batched on-chain
settlement. Full smoke test passed 8/8 on production.

Contact

[email protected]

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