Server has 10 tools with consistent naming (verb_noun pattern) and documented schemas. However, descriptions are generic and lack LLM-optimization guidance. Parameter descriptions exist but are minimal (avg ~40 chars). Output schemas are not documented in the source. Error handling is absent, no recovery guidance, categorization, or actionable error messages. Security: no evidence of input validation, rate limiting, or permission checks. Composition is reasonable (single responsibility per tool), but tools lack chaining metadata (e.g., calculate_ats_match returns no resume_id for downstream tools).

• Document output schemas for all 10 tools. Define structured return types (e.g., {score: number, matched_keywords: string[], missing_keywords: string[]} for calculate_ats_match) so LLMs can extract and chain results.
• Add try-catch blocks and error categorization to all external API calls (Groq, axios). Return {error: string, code: 'retryable'|'user_fixable'|'fatal', suggestion: string} instead of raw exceptions.
• Expand descriptions to 100 - 200 chars. Add WHEN to use, prerequisites, and dependencies. E.g., 'Calculate ATS match score between resume and job. Call after parsing both documents. Returns keyword overlap percentage and missing skills list.'
• Add input validation: max length for resume_text/job_text (e.g., 50KB), format checks for URLs, enum constraints for file types. Return clear validation errors: 'resume_text exceeds 50KB limit. Truncate or split into multiple calls.'
• Include chaining IDs in responses. E.g., parse_resume returns {text: string, resume_id: string, file_type: string}; calculate_ats_match returns {score: number, resume_id: string, job_id: string} so downstream tools can reference them.