get_top_search_keywordsList the website's top search keywords with clicks, impressions, CTR and average position. Great for finding quick wins (positions 5-15 with high impressions). Dates are YYYY-MM-DD; defaults to the last 30 days.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| end_date | No | ||
| start_date | No | ||
| website_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only and idempotent, so the description does not need to repeat that. The description adds value by specifying the output fields (clicks, impressions, CTR, position) and the date default behavior. There is no contradiction with annotations, and it provides useful context beyond the structured data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (three short sentences) and front-loaded with the core purpose. Each sentence serves a distinct function: what it does, why it's useful, and date format. There is no filler or repetition of schema information, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters and no output schema, the description covers the essential: purpose, metrics, use case, and date format. The only gap is the lack of explanation for the 'limit' parameter, which might confuse an agent about its effect. Overall, it is fairly complete for a simple read-only list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explicitly explains date format and defaults for start_date and end_date, which is helpful. However, it does not explain 'limit' (meaning or behavior), and 'website_id' is only implied. Thus it partially compensates but leaves ambiguity for the limit parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List'), a resource ('website's top search keywords'), and the exact metrics returned (clicks, impressions, CTR, average position). It also adds a concrete use case ('finding quick wins positions 5-15') that distinguishes it from broader tools like get_search_performance. This is very clear and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear context for when to use it ('Great for finding quick wins'), and it explains date handling. However, it does not explicitly mention alternatives or when not to use it; it relies on the implicit differentiation from sibling tools. This is good guidance but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools target a distinct resource and action, making selection straightforward. Minor overlap exists between get_ai_search_traffic and get_search_performance's AI-assistant traffic section, and among the three search-performance reporting tools, but descriptions are clear enough to avoid major confusion.
All tools follow a consistent lowercase snake_case verb_noun pattern: check_, generate_, get_, list_, publish_, request_, run_. The verbs are descriptive and every noun object clearly identifies the resource being acted on.
At 18 tools the set is slightly above the ideal 3-15 range, but the scope is broad enough to justify it: website management, article generation, publishing, search analytics, AI search, indexing, backlinks, and auditing. No tool feels redundant enough to remove.
The surface covers the main SEO lifecycle well: websites, keyword plans, articles, publishing, search performance, indexing, backlinks, and audits. Obvious gaps are article update/delete operations and website removal, but agents can work around these for most core workflows.