get_ai_search_trafficShow traffic the website receives from AI assistants and AI search (ChatGPT, Perplexity, Claude, Gemini, etc.): sessions, engaged pages and per-source breakdowns. 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 declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context: date format is YYYY-MM-DD and it defaults to the last 30 days. It doesn't describe pagination or response structure, but the annotations lower the burden.
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 two sentences with no fluff. The primary behavior is front-loaded, followed by concrete output examples and date handling. Every sentence adds useful information.
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 read-only report tool with strong annotations, the description covers the core purpose, output metrics, date format, and default range. There is no output schema, but the description names the key return components. Minor gaps like limit semantics remain, but the definition is largely sufficient.
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 partially covers the date parameters by specifying format and default behavior, but it does not explain website_id or limit semantics. This leaves half the parameters under-documented.
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 and resource: it shows traffic from AI assistants and AI search sources, with concrete examples (ChatGPT, Perplexity, Claude, Gemini) and specific metrics (sessions, engaged pages, per-source breakdowns). This clearly distinguishes it from sibling tools like get_search_performance or get_website_stats.
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 clear context for when the tool is relevant: measuring AI-assistant and AI-search traffic. It does not explicitly name alternatives or state when not to use it, but the AI-source scope is specific enough to imply the intended use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.