Server presents three well-named, read-only tools with comprehensive parameter descriptions. Naming follows verb_noun convention (get_growth, get_time_series, get_top_trends) clearly signaling intent. Descriptions are present and detailed (200-300+ chars), explaining WHAT each tool does, WHEN to use it, and dependencies between tools. Parameter descriptions are exceptionally thorough, including format requirements, valid values, and real-world examples (e.g., npm package names must be case-sensitive, Android bundle IDs required for app tools). Tool annotations correctly declare readOnlyHint and idempotentHint. However, output schemas are not documented, callers cannot see what fields to expect in responses. Error handling guidance is embedded in descriptions ('tell the user their plan limit is reached') but not structured as recovery patterns. No enumerated constraints in schema, parameter values are constrained only via description text, not JSON Schema enums.

• Document output schemas for all three tools. Specify what fields each returns (e.g., 'get_growth returns {keyword, source, periods: [{window, growth_value, volume?}]}'), including nullable fields and data types. This enables LLMs to plan downstream calls and extract values reliably.
• Convert parameter constraints to JSON Schema enums. Define 'percent_growth' items as enum ['7D', '1W', '14D', ...] instead of listing in description. Define 'type' in get_top_trends as enum with 20+ feed names. This is machine-parseable and prevents hallucinated values.
• Add structured error codes to return values. Instead of relying on error message parsing, return {error: 'QUOTA_EXCEEDED', message: '...'} so LLMs can programmatically detect retryable vs fatal errors.
• Clarify pagination limits. State in get_top_trends description: 'limit: max 200 (default 25). Results beyond this limit are not returned; pagination not supported beyond offset 5000.'
• Split get_top_trends category logic: either make category always required (simpler for LLM), or explicitly partition the tool into 'get_top_trends_global' (no category) and 'get_top_trends_category' (category required). The current conditional is easy to misuse.
• Add per-tool examples of valid invocations in docstrings (not parameter descriptions). E.g., for get_growth: 'Example: get_growth(keyword="ChatGPT", source="google search, youtube", percent_growth=["12M", "3M"]) returns growth percentages across both sources.'
• Document rate limits and quota model. Specify: max requests per minute/hour, how quota is consumed, and what 'monthly quota is used up' means in concrete terms.