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Scored across 25 tools
Tools are mostly distinct despite the large set. The five financial analysis tools (bear_vs_bull, earnings_analysis, insider_signal, stock_thesis, valuation_snapshot) target different aspects of stock research, though an agent might briefly confuse stock_thesis with bear_vs_bull. Text extraction tools are well-differentiated by scope (general entities vs. contracts vs. meetings).
Approximately 80% of tools follow a consistent verb_noun pattern (e.g., extract_contract_clauses, generate_schema, normalize_address). However, the financial analysis cluster breaks convention with noun_noun naming (earnings_analysis, insider_signal, stock_thesis, valuation_snapshot) or noun_vs_noun (bear_vs_bull), creating a mixed convention that reduces predictability.
With 25 tools, this sits at the upper bound of 'borderline heavy' per the rubric. While each tool serves a distinct utility purpose (text extraction, stock analysis, data conversion), the breadth covers many unrelated domains (finance, cron syntax, image metadata, address normalization), making it a 'kitchen sink' collection that risks selection paralysis without being completely unmanageable.
For a general agent utility belt, the surface covers the targeted domains reasonably well. The financial cluster provides fundamental analysis capabilities (thesis, valuation, earnings, insider activity, bull/bear cases). Text extraction covers general entities, contracts, and meetings. Missing minor operations like json_to_csv or additional image processing don't create critical dead ends given the toolset's utility-focused nature.