list_websitesList the websites in the user's SEOPro AI account.
Returns each website's numeric website_id (needed by most other tools),
URL, platform and autopilot status. Call this first.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is established. The description adds valuable behavioral context beyond that by specifying exactly what data is returned (website_id, URL, platform, autopilot status) and the positional role of the tool as the first call to make.
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 tight sentences with no filler. The first sentence states the core action, and the second provides only essential return-field info and the 'call this first' directive. Every sentence earns its place.
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 no-parameter, read-only list tool, the description is complete. It names the output fields (website_id, URL, platform, autopilot status), explains why the output matters for other tools, and gives sequencing guidance. With no output schema, this description sufficiently covers what an agent needs to know to call the tool correctly.
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?
The tool has zero parameters, so the baseline is 4. There are no parameter semantics to clarify, and the description correctly focuses on the output and usage context rather than inventing parameter details.
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 opens with a specific verb ('List') and a clear resource ('the websites in the user's SEOPro AI account'), making the tool's purpose immediately obvious. It also differentiates itself from siblings like list_articles by focusing on websites, and the note 'Call this first' establishes its role as a discovery entry point.
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 usage context: 'Call this first' and explains that the returned website_id is needed by most other tools. This tells an agent when to use the tool, though it does not explicitly mention alternatives or scenarios where the tool should not be used.
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.