Select a tool

Use this before planning a tool call. Include the request and the available tool descriptions, then let the decision choose one tool or none. Open this recipe in the playground, or set D1_API_KEY as in the quickstart and run the same request:

curl --fail-with-body --silent --show-error https://console.sqwish.ai/v1/decide \
  -H "Authorization: Bearer $D1_API_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
  "model": "sqwish-d1-core",
  "context": {
    "request": "Can you book us a table for four at Dishoom King's Cross this Friday at 7pm? Put it under Priya."
  },
  "decisions": [
    {
      "id": "tool",
      "kind": "single",
      "question": "Which tool should the agent call for this request?",
      "outcomes": {
        "search_restaurants": "Find restaurants by area, cuisine, price or opening hours, and return their names and ratings.",
        "book_table": "Reserve a table at a named restaurant for a party size, date and time, under a guest's name.",
        "get_directions": "Give walking, driving or public transport directions between two places.",
        "send_message": "Send a text or chat message to a contact.",
        "none": "None of these tools does what the user asked."
      },
      "abstain": {
        "min_probability": 0.5
      }
    }
  ]
}
JSON

Use the result

Read decisions.tool.action. If abstained is true, use the full planner or ask for clarification. If the action is none, continue without a tool. Otherwise pass the selected tool’s name to your planner so it can construct and validate arguments. Choosing a tool does not validate its arguments or authorize its execution. Check permissions, required fields and side effects separately. The tool-risk example evaluates a proposed call at that later boundary. The recipe is a single-choice task. Requests requiring multiple tools need a planner or another step. Evaluate overlapping descriptions, unsupported requests and changes to your tool catalogue; do not assume a score from these five sample tools transfers to a larger registry.

What was evaluated

The historical measurement covers the declared tool choice, not successful execution or task completion. Compare selection accuracy and abstention with your planner on your own requests.

Historical evaluation on 2026-09-28, using sqwish-d1-core. These measurements describe that checkpoint and dataset, not current production performance or an accuracy guarantee.

Dataset: BFCL live (multiple functions and irrelevance) (Apache-2.0).
Split: live_multiple and live_irrelevance (test only), tool sets whose hash falls below 0.5.
Sample: 1,000 rows, 1,000 measured decisions.
Measured decision IDs: tool.
Wording: hand-written.
Checkpoint SHA-256: dd420ca652cfaa10279eabb54d15afd50fe9d24d357498c4dbc512b1c3d73bb2.

Metric	Value	Recorded interval
accuracy	0.789	0.7278 to 0.847
log loss	0.5282	0.4186 to 0.6354
brier	0.3142	0.2353 to 0.3981
ece	0.0987	0.0541 to 0.1558

The majority-class baseline has accuracy 0.414; the class-prior baseline has log loss 2.9656. Only the decision IDs listed above were measured. Dataset labels, class balance and wording affect these results. The intervals do not measure distribution shift. Re-evaluate with your own cases, including ambiguous and out-of-scope inputs.

Improve it for your application

Keep the decision IDs and outcome order stable while evaluating changes. Review a dataset, tune the wording, and compare the result against your current policy before changing production. Check fallback so you know which model actually answered.

Build with D1

Evaluate and retrieve

Use the result

What was evaluated

Improve it for your application

​Use the result

​What was evaluated

​Improve it for your application

Use the result

What was evaluated

Improve it for your application