PLCs.ai

ai.plcsv1.0.0更新於 Oct 1, 2026

Query Allen-Bradley and Siemens PLC projects, live tag values, and analyses in plain English.

已驗證Streamable HTTP可網頁執行AI & ML

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 PLCs.ai，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "plcs-ai": {
      "type": "http",
      "url": "https://mcp.plcs.ai/mcp"
    }
  }
}

README

PLCs.ai MCP Server

Connect Claude, Cursor, Codex, or any MCP-compatible agent to your PLC projects.

PLCs.ai is the AI platform purpose-built for PLC engineers: upload an Allen-Bradley (Studio 5000), Siemens (TIA Portal) or CODESYS V3 project and understand, troubleshoot, document, and safely generate code for it in plain English. This MCP server gives any AI agent governed access to that same capability — your projects, live tag values, and analyses — with per-tool permission scopes.

Remote server: https://mcp.plcs.ai/mcp (Streamable HTTP · OAuth or API key) Full documentation: https://www.plcs.ai/developer/mcp

Quick start

Claude Code

bash
claude mcp add --transport http plcs-ai https://mcp.plcs.ai/mcp

Then run /mcp and choose Authenticate to sign in with your PLCs.ai account. (Or pass an API key: --header "Authorization: Bearer plck_live_…".)

Claude Desktop

Settings → Connectors → Add custom connector → URL https://mcp.plcs.ai/mcp → Connect and sign in. For API-key or on-prem setups, use the mcp-remote bridge:

json
{  "mcpServers": {    "plcs-ai": {      "command": "npx",      "args": ["-y", "mcp-remote", "https://mcp.plcs.ai/mcp",               "--header", "Authorization: Bearer plck_live_…"]    }  }}

Cursor / Codex / other MCP clients

See per-client guides: https://www.plcs.ai/developer/mcp — covers Cursor, Codex, and any client that speaks remote MCP or can run mcp-remote.

What the agent can do

17 tools, permission-scoped per API key or OAuth grant:

AreaTools
Discoverplcs_health, plcs_list_projects, plcs_get_project, plcs_get_project_source
Askplcs_ask_project — a cited answer from the PLCs.ai assistant, not raw source to reason over
Live plant dataplcs_get_live_values, plcs_get_tag_history, plcs_request_live_tags
Analyzeplcs_get_project_analysis (dead code, race conditions, missing handshakes, interlock audits, cycle-time bottlenecks, signal tracing)
Proposeplcs_propose_code — a plan you approve, then a reviewable proposal; nothing is saved without review
Version & exportplcs_save_version, plcs_stage_upload, plcs_export_plc, plcs_export_pdf, plcs_get_export, plcs_download_export
Embedplcs_mint_embed_token — short-lived, read-only, project-scoped iframe tokens

The agent reasons over projects your organization already owns. Onboarding a machine is a deliberate act in the app or through a version-source connector, so there is no create-a-project tool — an agent can read, ask, propose and save versions, but never add a project.

Example prompts

  • "List my PLC projects and check which ones have live data connected."
  • "Why won't Conveyor 3 restart after a stop? Trace the interlocks."
  • "Read the latest analysis on the Palletizer project and summarize what's safe to remove."
  • "What did tag Motor1_Enable do in the last hour?"
  • "Draft a fix for the missing handshake between Conv001 and Rob010 — proposal only."

Safety model

  • Every tool call is authenticated and scoped to your organization; the credential is the tenant.
  • Proposing code is two steps — a plan you approve, then the change — and it returns a proposal either way; persisting a new version is a separate, separately-scoped action.
  • Customer data is never used to train AI models. Data handling →

Requirements

© PLCs.ai · Terms · Privacy · Questions: [email protected]

來源：README.md，提交 9d9f6da

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版本歷史

1
  1. v1.0.0最新Sep 16, 2026