Single tool 'collect_feedback' with well-structured input schema and comprehensive description. The tool demonstrates strong parameter documentation with detailed requirements for the work_summary field. However, lacks explicit output schema documentation, error handling guidance, and tool annotations. The description is exceptionally long (800+ chars) which exceeds best practice but is justified by the critical nature of the work_summary requirements. Parameter naming follows verb_noun conventions ('collect_feedback'). Tool accepts natural identifiers (project_name, project_path) which aligns with chat-data-model patterns. The schema uses proper JSON Schema with type definitions and descriptions for all parameters.

• Add an explicit output schema documenting what collect_feedback returns: { success: boolean, feedbackId?: string, timestamp?: string, message?: string, error?: string }.
• Reduce main description to ~150 characters and move detailed requirements (Markdown formatting, section structure) into a separate extended description or documentation link.
• Add destructiveHint: true to tool definition since it persists user feedback (write operation).
• Document error cases in description: 'Returns error if work_summary < 500 chars, project_path invalid, or UI times out after 60s. LLM should retry with expanded summary or validate project_path.'
• Add minLength and maxLength constraints to work_summary in the schema: { type: 'string', minLength: 500, description: '...' }.
• Consider adding an optional 'dry_run' parameter to preview feedback without persisting, or a separate 'preview_feedback' tool for review before submission.
• Add idempotentHint if feedback can be safely resubmitted with the same content (e.g., by feedbackId), or false if each call creates a new feedback entry.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry