get_articleRead a single article by blog_id. By default returns metadata and a short excerpt; set include_content=true for the full HTML content.
| Name | Required | Description | Default |
|---|---|---|---|
| blog_id | Yes | ||
| include_content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral context beyond the readOnly/idempotent annotations by disclosing the default return value (metadata and a short excerpt) and how include_content changes the response to full HTML. This helps an agent predict output without seeing an output schema. It omits error and auth details, but annotations already cover the safety profile.
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 core action is front-loaded, followed by a clear explanation of the optional parameter's effect. Every clause 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 simple read-only tool with two parameters and helpful annotations, the description sufficiently covers invocation behavior and output options. It does not enumerate metadata fields, but with no output schema that would be a minor enhancement rather than a critical gap.
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 schema description coverage at 0%, the description must explain parameters itself. It does this well: blog_id is identified as the article key, and include_content is explicitly tied to returning full HTML. The schema already provides types and defaults, so the description adds the necessary semantic meaning.
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 tool reads a single article by blog_id, using a specific verb and resource. It also explains the default output and the optional full-content mode, which conveys the tool's scope. It does not explicitly contrast with siblings like list_articles or get_article_status, but the 'single article' phrasing makes the distinction reasonably clear.
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 implies the tool is for retrieving one article, optionally with full HTML content. It does not explicitly mention when to prefer a different tool such as list_articles for multiple articles or get_article_status for status-only checks. The guidance is adequate but relies on inference.
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.