propose_change
propose_change
propose_change
Creates, updates, or deletes Ada entities (knowledge articles, coaching, custom instructions, test cases) and triggers test runs.
Call get_improvement_guide before proposing changes so the assistant picks the right entity type and follows Ada’s improvement best practices.
propose_change uses a three-phase flow so field schemas are discovered and changes are confirmed before they are applied.
Every write goes through three steps:
entity_type and operation only (no fields). The tool returns available fields, types, and descriptions for that combination.fields populated (or entity_id only for delete on test cases). The server returns a confirmation preview with a human-readable summary and Confirm/Cancel options — present these to the user. No change is applied yet.confirmed: true and the same arguments. The tool validates inputs and applies the change.This structure prevents accidental writes and surfaces what each operation requires before acting.
Calling an unsupported entity_type × operation combination returns an explicit “not supported” error. Use send_feedback if a capability you need is missing.
New knowledge articles are created with enabled: false by default so they don’t affect live traffic until explicitly enabled. Enable them from the Ada dashboard after review.
On update, every field is optional — supply only the fields you want to change. Pass the article ID via entity_id.
Only update is supported — coaching entries are created from the Ada dashboard or through conversation-level coaching workflows.
Pass the coaching ID via entity_id. Use search_coaching to find coaching IDs.
On update, every field is optional. Pass the custom instruction ID via entity_id.
Each test case describes how the AI Agent should respond to a specific scenario and what evaluation criteria determine a pass or fail.
On update, every field is optional. Pass the test case ID via entity_id. On delete, no fields are needed — pass only entity_id.
Creating a test_run triggers execution for the specified test case IDs. Use get_test_runs afterward to retrieve pass/fail status, evaluation criteria outcomes, and rationale.