| name | assess |
| description | Test Note content against natural language predicate using LLM (returns boolean). Use to determine if a text Note meets criteria. |
| type | python |
| flattens_collections | true |
| schema_hint | [object Object] |
| examples | {"type":"if","condition":{"type":"tool_condition","tool":"assess","target":"$content","predicate":"contains citations?"},"then":[...]}, {"type":"assess","target":"$document","predicate":"discusses AI safety?","out":"$is_relevant"} |
Test Note
Universal boolean test tool that evaluates natural language predicates against Note content using LLM.
Purpose
Provides flexible conditionals for plan control flow. Use when:
- No specialized condition exists
- Complex reasoning over content required
- Ad-hoc validation needed
Input
predicate: Natural language question (e.g., "contains citations?", "is valid JSON?", "mentions neural networks?")target: Note content to test
Output
Returns "true" or "false" as string (for use in tool_condition).
Usage Examples
Test for citations:
{"type":"if","condition":{"type":"tool_condition","tool":"assess","target":"$paper","predicate":"contains academic citations?"},"then":[...]}
Check data validity:
{"type":"if","condition":{"type":"tool_condition","tool":"assess","target":"$data","predicate":"is valid JSON with 'results' field?"},"then":[...]}
Content filtering:
{"type":"if","condition":{"type":"tool_condition","tool":"assess","target":"$text","predicate":"discusses machine learning or AI?"},"then":[...]}
Guidelines
- Be specific: Clear predicates yield accurate results. "Contains author and year metadata?" > "has metadata?"
- Binary questions: Phrase as yes/no questions for clearest results.
- Prefer specialized conditions: Use built-in conditions (contains, equals) when possible - they're faster.
- Cost aware: Every call hits LLM. Use judiciously in loops.
Performance
- Fast execution: ~1-2 seconds per test
- Logged for usage analysis