Server has 4 tools with complete Zod schemas and descriptions. Naming follows verb_noun pattern (analyze_, compare_, benchmark_, find_). Descriptions are substantive (150-250 chars) and explain WHEN to use each tool. However, parameter descriptions lack actionable constraints (no enums for market segments, no format specs for URLs, no min/max for optional fields). Output schema is undocumented, responses are free-text LLM completions with no structured fields, forcing agents to parse unstructured text. Error handling is absent; LLM failures return raw text with no recovery guidance. No tool annotations (readOnlyHint, idempotentHint). Composition is sound, each tool has one responsibility and tools chain naturally (analyze → compare → benchmark → find_gaps). Baseline: avg tool description 194 chars (these are ~180-220, within range); avg params 4 (these have 3-5, on target); but 0% have output schemas documented and 0% have error guidance.

• Document output schema for each tool. E.g., analyze_competitor should return {competitor_name, pricing_tiers: [{name, price, features}], strengths: [], weaknesses: [], positioning: string, differentiation_angles: []}. Use JSON Schema in tool definition.
• Add error handling wrapper: catch Groq API errors and return {error: string, recovery_hint: string}. E.g., 'API timeout. Retry with a shorter context or simpler query.' This guides agent recovery.
• Add format constraints to string parameters. E.g., competitors: 'Comma-separated list (e.g., "Notion, Obsidian, Roam"). Max 5 competitors per call.' This prevents malformed input.
• Add tool annotations: readOnlyHint=true and idempotentHint=true to all tools. This signals to agents that calls are safe to retry and do not modify state.
• Cap response length and document it. E.g., 'Returns analysis up to 2000 tokens. For deeper analysis, call with more specific context.' Prevents context window overflow.