All 5 tools have minimal, generic descriptions (10-30 chars) that fail to explain WHAT the tool does, WHEN to use it, or what it returns. Input schemas are present but trivial: all tools accept a single 'input' string parameter with no type validation, format guidance, or constraints. No output schemas documented. No error handling guidance. Tools lack verb-based naming (e.g., 'job_description_generator' should be 'generate_job_description'). No parameter descriptions beyond 'Input data (JSON)'. No evidence of idempotency, pagination, or composition patterns. This server reads like a thin wrapper around a remote API with no agent-optimization.

• Rename tools to verb-based patterns: 'generate_job_description', 'parse_resume', 'analyze_training_needs', 'generate_kpi', 'get_training_certifications'. This helps LLMs infer intent from the name alone.
• Expand each tool description to 50 - 150 characters explaining WHAT it does, WHEN to use it, and what it returns. Example: 'Generate a job description from role title, department, and responsibilities. Returns title, summary, requirements, and qualifications.'
• Document input schema with explicit JSON structure. Example for generate_job_description: {role_title: string, department: string, responsibilities: [string], experience_years: number}. Add constraints (min/max, enums) and examples.
• Document output schemas for each tool. Example: job_description returns {title, summary, key_responsibilities, required_qualifications, preferred_qualifications, salary_range}. This enables tool chaining.
• Add error handling: validate remote responses, catch HTTP errors, and return actionable messages. Example: 'Failed to generate job description: invalid role_title. Provide a job title like "Senior Software Engineer" or "Product Manager".' This guides LLM recovery.