check_ai_citationsCheck whether AI assistants (ChatGPT, Claude, Gemini, Perplexity) mention or cite a website when answering questions in its niche. Takes 1-3 minutes; results are cached for 7 days.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are all false, so the description carries the burden. It discloses that the operation takes 1-3 minutes and results are cached for 7 days, which is useful behavioral context. However, it does not clarify whether the call is synchronous or asynchronous, whether it triggers network requests, or any rate limits. It adds some transparency but leaves significant gaps.
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 extraneous words. The primary purpose is front-loaded, and the operational details (duration, caching) are delivered in the second sentence. Every element earns its place; it is appropriately sized and well-structured.
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?
Despite the tool's complexity (checking multiple AI assistants, likely network calls), the description omits critical operational details: what the output looks like (no output schema), how to interpret results, whether it returns a job ID for async polling, and what happens on failure. The timing and caching info hint at async behavior but do not explain the workflow. This is incomplete for an agent to invoke and interpret successfully.
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?
With only one parameter (url) and 0% schema description coverage, the description must compensate. It indirectly clarifies that the url refers to the website being checked, but does not provide format details, examples, or validation rules. The single parameter is self-evident enough to warrant a baseline score, but the description adds minimal explicit value beyond the schema's field name.
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 clearly states the verb 'Check' and the specific resource: whether AI assistants (ChatGPT, Claude, Gemini, Perplexity) mention or cite a website. It distinguishes this tool from siblings like get_ai_search_traffic or generate_article by focusing on citation behavior across named assistants. No ambiguity about what it does.
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 provides no guidance on when to use this tool instead of alternatives. It does not mention any exclusions, prerequisites, or contrasting scenarios. The presence of siblings like get_ai_search_traffic suggests potential overlap, but the description does not address when one is preferred over the other.
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.