abstract_toolserver

The abstract_* ecosystem exposed as an API-callable AI toolset — every tool is a plain Python function turned into a self-describing HTTP endpoint by abstract_flask. A portable tool layer any model (Claude, hugpy, …) can drive over HTTP instead of being bound to one runtime's tool harness.

Run it

pip install abstract_toolserver          # + the extras you want to expose
python -m abstract_toolserver            # HOST/PORT/DEBUG from TOOLSERVER_* env

from abstract_toolserver import get_toolserver_app
app = get_toolserver_app()               # a normal Flask/WSGI app

Self-describing surface

The app auto-mounts introspection endpoints (from abstract_flask):

Call a tool with JSON; unknown keys are pruned to the function signature, and the reply is {"result": ...} (or {"error": ...}). Discover-and-dispatch from a client is already provided by abstract_apis.make_endpoint_call.

curl -s localhost:5000/fs/count_tokens -d '{"text":"hello world"}'
# {"result": 2}
curl -s localhost:5000/db/schema           # {"result": {table: [cols...]}}
curl -s 'localhost:5000/db/query?help=true'

Tool categories

Safety gates

Backends load lazily, so the server boots on a headless box and a missing backend errors only when its tool is called. Beyond that:

• /db/query — read-only gate: rejects anything that isn't a single SELECT/WITH, blocks stacked statements and data-modifying keywords. Use /db/fetch (identifier-composed, params-not-SQL) as the default read path.
• /sys/run_cmd — disabled unless TOOLSERVER_CMD_ALLOWLIST=ls,grep,… is set; only allowlisted binaries run.
• /fs/read_file · /fs/write_file — local-only; the underlying SSH/remote kwargs are never exposed at the boundary.
• /ui/click_verify — the click→observe→verify loop: locate (text or image template) → click → re-capture → report whether the screen (or a region) changed. The half most tool APIs lack.

Configuration