Academic Data Hunter has 4 tools with schemas and basic descriptions, but suffers from critical naming and description gaps. Tool names lack action verbs (run_round, validate_round, benchmark_eval are vague). Parameter descriptions are trivial (e.g., 'data', 'value col') with no guidance on format, constraints, or dependencies. No output schemas documented. Error handling is minimal. The server is STDIO-only, which caps protocol readiness at 50 regardless of definition quality.

• Rename tools with clear action verbs: 'generate_task_file' (not 'run_round'), 'validate_dataset_consistency' (not 'validate_round'), 'score_research_coverage' (not 'benchmark_eval').
• Expand parameter descriptions to 50 - 150 characters each. Example: 'data' → 'Path to CSV or Excel file containing research dataset (required format: .csv or .xlsx with headers in row 1)'. Include constraints, valid ranges, and dependencies.
• Document output schemas in tool descriptions. Example: 'Returns JSON object with fields: task_file_path (string), missing_records_count (integer), estimated_hours (float), next_steps (array of strings)'.
• Add error handling guidance to descriptions. Example: 'If validation fails, returns error with field name and constraint violated. Retry after correcting data format or call validate_dataset_consistency() first to diagnose issues.'
• Implement per-tool error responses that classify failures as retryable, user-fixable, or fatal, with actionable next steps (e.g., 'Invalid year_start: must be ≥ 1900 and ≤ year_end. Try year_start=2000, year_end=2024').