HireMe MCP demonstrates solid tool design with clear naming, comprehensive parameter schemas, and good descriptions. All 5 tools follow verb_noun convention (get_*, search_*, submit_*). Input schemas are well-defined using Zod with type constraints, min/max lengths, and field descriptions. However, output schemas are not formally documented, responses are plain text wrapped in a generic content structure, making it difficult for LLMs to parse structured data downstream. Error handling is minimal; handlers do not return actionable recovery guidance. The submit_project_brief tool includes input sanitization (clean() function), which is good security practice. Tool descriptions average ~120 chars, within the 10-1024 baseline, but lack explicit WHEN/WHY guidance for tool selection.

• Document output schemas for all tools. Define structured response objects (e.g., {profile: {name, role, stack, contact}, booking_url}) instead of plain text. This enables LLMs to extract and chain data reliably.
• Add input validation and error recovery guidance. E.g., if contact is invalid, return 'Invalid contact format. Expected email (user@domain.com) or WhatsApp (+1234567890).' This lets LLMs self-correct.
• Enhance tool descriptions with explicit WHEN/WHY. E.g., 'Call get_profile first to see Djaouad's full background and stack. Use search_projects to find proof of specific skills (e.g., RAG, payment integration).'
• Add pagination to search_projects. Accept limit (1-20, default 10) and offset parameters. Return {results: [...], total: N, has_more: bool} so LLMs can iterate if needed.
• Constrain contact parameter in submit_project_brief. Add regex pattern or enum hint in description: 'Email (user@domain.com) or WhatsApp (+1-10 digits).' Validate server-side and return clear error if invalid.