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Agentic lead scoring and conversion velocity evaluator that calculates behavioral engagement scores and predicts high-intent checkout triggers, outputting next best marketing actions.
Single tool with critical definition gaps. Tool name 'evaluate_lead_score' lacks action verb prefix (should be 'score_lead' or 'calculate_lead_score'). Description is present (58 chars) but generic and lacks WHEN/WHY guidance. Input schema visible with 4 parameters, all typed with defaults, but parameter descriptions are minimal (10-30 chars, below 72-char baseline). No output schema documented, LLM cannot plan downstream actions. No error handling, validation guidance, or recovery paths. Tool definition is directly visible in code (not inferred), so no cap applies, but quality is poor across all dimensions.
Scores customer engagement velocity and outputs next best marketing action.
Tool name lacks action verb. 'evaluate_lead_score' is passive; should start with verb like 'score_lead', 'calculate_lead_score', or 'predict_lead_conversion'. LLMs infer intent from verb, passive names reduce clarity.
No output schema documented. Tool returns 5 fields (evaluation_id, total_intent_score, funnel_stage, next_best_action, urgency_level, conversion_probability_pct) but LLM has no way to know this. Agents cannot plan downstream actions or extract fields reliably.
Parameter descriptions are too brief (10-30 chars vs 72-char baseline). 'Session dwell time in seconds' lacks context on why this matters or what range is expected. 'Number of page views in session' does not explain how this influences scoring.
No error handling or validation guidance. Tool accepts any integer for session_dwell_time_sec and page_views_count with no bounds. What happens if negative values are passed? No recovery paths documented.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 40 | 2026-07-28+ | v2 |
Tool description lacks WHEN/WHY context. 'Scores customer engagement velocity and outputs next best marketing action' does not explain when to call this vs other tools, what prerequisites exist, or what the output enables. LLM cannot determine if this is the right tool for a given task.