Critical gaps across all dimensions. Tool definitions lack visible input schemas in source code, cannot verify parameter types, constraints, or descriptions. Tool descriptions are present but generic (10-50 chars typical), providing minimal LLM guidance. No evidence of structured output schemas, error handling guidance, or parameter validation rules. 40 tools defined but implementation details not inspectable from provided source. Naming follows verb_noun convention (positive), but without schema visibility, cannot assess parameter quality or composition patterns.

• Add explicit JSON Schema definitions for every tool parameter. Include type, description, enum constraints, min/max bounds, and format patterns. Example: { 'type': 'string', 'description': 'Module name (2-50 alphanumeric chars)', 'pattern': '^[a-zA-Z0-9_-]{2,50}$' }
• Expand tool descriptions to 80-150 characters. State WHAT the tool does, WHEN to call it (vs similar tools), and what it returns. Example: 'Find all usages of a function or class across the codebase. Call this before refactoring to assess impact. Returns file paths, line numbers, and context snippets.'
• Document output schemas for all tools. Specify field names, types, and whether results are paginated. Example: { 'type': 'object', 'properties': { 'modules': { 'type': 'array', 'items': { 'type': 'object', 'properties': { 'name': { 'type': 'string' }, 'path': { 'type': 'string' } } } }, 'total': { 'type': 'integer' } } }
• Add parameter descriptions to every input. Explain what each parameter controls, valid ranges, and dependencies. Example for 'limit': 'Maximum results to return (1-100, default 20). Larger limits may timeout on large codebases.'
• Implement error recovery guidance. Return structured errors with actionable next steps. Example: { 'error': 'Module not found', 'suggestion': 'Call list_modules() to see available modules', 'available': ['auth', 'api', 'ui'] }