Three tools with complete input schemas and descriptions, but significant quality gaps. Tool names lack action verbs (t1_translate, t2_evaluate, checksum are noun-heavy). Descriptions are adequate (100-200 chars) but lack WHEN/WHY guidance for LLM selection. Parameters have types and descriptions, but no enums for constrained inputs (weight_hint accepts free-form strings despite 5 canonical options). Output schemas are not documented, LLMs cannot plan downstream calls. Error handling is absent from visible code. No security considerations (secrets, permissions, audit). Composition is reasonable (three distinct concerns), but tools are tightly coupled to a proprietary protocol with limited reusability.

• Rename tools with action verbs: translate_to_t1_structure, evaluate_confidence_t2, validate_structure_checksum. This helps LLMs infer intent from the name alone.
• Add enum constraints for weight_hint and locale parameters. Replace free-form strings with explicit allowed values to prevent hallucination.
• Document output schemas for all three tools. Specify what fields are returned, their types, and what downstream tools can expect. Example: t1_translate returns {structure: string, assumptions: array, evidence_levels: object}.
• Implement error handling with recovery guidance. Validate inputs early and return actionable error messages. Example: 'Invalid weight_hint: got "efficiency", must be one of: 事实优先, 効率優先, 成本優先, 鲁棒性優先, 通用優先. Did you mean "效率优先"?'
• Add WHEN/WHY guidance to tool descriptions. Clarify when to call t1_translate vs t2_evaluate vs checksum. Example: 'Call t1_translate first to structure a reasoning task; then call t2_evaluate to assess the output quality; finally call checksum to validate structural integrity.'