Four tools with complete Zod schemas and descriptions. Naming is action-verb based (research_, write_, score_) and clear. Descriptions are adequate (100-200 chars) but lack LLM-optimized guidance on WHEN to use each tool vs. alternatives. Parameters are well-typed with enums for constrained inputs (goal, tone, sequence_position, type, channel). However, output schemas are undocumented, responses are free-text LLM generations with no structured field definitions, forcing agents to parse unstructured text. Error handling is absent; no guidance on retryability or failure modes. No tool annotations (readOnlyHint, destructiveHint). Missing dependency hints (e.g., 'call research_prospect first to gather context for write_cold_email').

• Document output schemas for all tools. E.g., research_prospect should return {pain_points: string[], personalization_hooks: string[], best_angle: string, key_message: string} instead of free text. This enables agents to extract and reuse specific fields.
• Add error handling with recovery guidance. Wrap Groq API calls in try-catch; return {error: string, retryable: boolean, suggestion: string} on failure. E.g., 'API rate limit exceeded. Retry in 30 seconds or try a simpler prompt.'
• Add tool annotations to all tools: readOnlyHint: true (no side effects), idempotentHint: true (safe to retry). This signals to agents that these tools are safe and can be called multiple times.
• Enhance descriptions with composition hints. E.g., write_cold_email: 'Use after research_prospect to incorporate personalization hooks. Generates subject line + email body.'
• Add per-parameter validation with actionable errors. E.g., if goal is invalid, return 'Invalid goal. Must be one of: book_call, get_reply, demo_request, referral, partnership.' instead of letting Zod throw a generic error.