This server exposes 4 tools with basic schemas and descriptions, but lacks critical production-grade polish. All tools have descriptions (10-50 chars, below the 50-200 char baseline for LLM optimization), and input schemas are present but incomplete. Parameters lack type constraints (enums, ranges), descriptions are generic, and output schemas are undocumented. No error handling guidance, no parameter validation rules, and no composition support (e.g., getTrends is a pure alias, not a distinct tool). The server wraps a third-party API (trendsapi) without adding agent-specific value like pagination, result limits, or recovery hints.

• Expand tool descriptions to 100-150 chars. Example: 'getTimeSeries: Fetch historical search volume for a keyword from Google Trends (or another source). Returns time-series data points with date and normalized value (0-100). Use this to analyze keyword popularity trends over time.'
• Remove getTrends or rename it to reflect a distinct operation (e.g., getRecentTrends for last 7 days, getTrendingKeywords for top keywords). Do not expose two tools that do the same thing.
• Add enum constraints to 'source' and 'data_mode' parameters. Document valid values in the parameter description. Example: 'source: Data source (enum: "google search", "youtube", "news", "google shopping", "google images"). Defaults to "google search".'
• Document output schemas inline or via a separate schema reference. Example: 'Returns array of TrendsDataPoint objects: [{date: ISO8601, value: 0-100, isPartial: boolean}, ...]'.
• Add error handling guidance to descriptions. Example: 'If the keyword is not found, the API returns a 404. Try a more general keyword or call getTopTrends to discover trending keywords.'
• Add pagination support (limit, offset) to getTopTrends and document result limits. Cap results at 50 items by default to avoid context window exhaustion.