Three tools with complete input schemas and descriptions, but descriptions are verbose (200+ chars) and lack LLM-optimized structure. Tool names lack action verbs (alternative_credit_score, mpesa_creditworthiness, credit_report_summary are noun-heavy). All parameters have types and descriptions, meeting baseline requirements. Output schemas are partially documented in code comments but not formally declared. Error handling is minimal, no recovery guidance or actionable error messages. Positive: tool annotations present (readOnlyHint=true); negative: no input validation, no pagination, no structured error responses.

• Rename tools with action verbs: 'estimate_credit_score', 'analyze_creditworthiness', 'generate_credit_report'. This signals intent to LLMs and aligns with verb_noun baseline (90% of A+ tools).
• Shorten descriptions to 50 - 150 chars. Move disclaimers to server-level instructions. Example: 'Estimate creditworthiness from M-PESA behavioral signals (demo only).' instead of the current 200+ char verbose text.
• Declare output schemas in fastmcp tool definitions. Use a return type annotation or explicit schema dict showing fields like {status: string, demo_credit_score: int, credit_tier: string, ...}.
• Add input validation with actionable error messages. Example: if months_as_mpesa_user > 240, return {error: 'Invalid months: got 999, must be 0 - 240'}. This enables LLM self-correction.
• Add enum constraints to string parameters. 'employment_type' in credit_report_summary should use JSON Schema enum: ['formal_employed', 'self_employed', 'informal', 'student', 'retired', 'unemployed'] rather than free-form text.