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toolchestrator

The Python client for Toolchestrator — connect a small local tool to your company's hub without moving it off the machine it runs on.

With this client a connected tool can:

  • register itself in the company registry (name, author, the problem it solves);
  • publish versioned JSON-Schema data models and sync records to the hub;
  • serve remote task actions over an outbound long-poll (the hub never connects into your machine);
  • read other tools' data through the hub with filtered queries;
  • invoke another tool's action and get the result back (tc.call);
  • expose its own local web UI through the hub (tc.serve_web).

Works on Python 3.9+; depends only on requests and websocket-client.

Install

pip install toolchestrator

Quick start

from toolchestrator import Toolchestrator

# one-time registration, with a personal token from your hub's Settings page
tc = Toolchestrator.register(
    "https://hub.example.com",        # your company's hub URL
    "tcu_...",                        # personal access token
    "invoice-radar", "Invoice Radar",
    description="Tracks vendor invoices and flags overdue ones.",
    sharing_scope="private",          # start private; widen deliberately
)

# afterwards, anywhere in the tool (loads .toolchestrator.json):
tc = Toolchestrator()
tc.sync_schema("invoice", invoice_schema)
tc.sync_data("invoice", rows, id_field="id")
tc.serve({"mark_paid": handle_mark_paid})   # execute tasks queued on the hub

The easiest way to connect a tool is to let an AI coding agent do the wiring: point it at your hub's connect guide (<your-hub-url>/skill) and ask it to "connect this tool to Toolchestrator". The hub URL always comes from you — the client never assumes one.

License

MIT © Stevica Kuharski

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