gsc_set_brand_termsSet custom brand terms (business name, product names, misspellings) to enable branded vs non-branded analysis in Search Console reports.
Set the words that make a query 'branded' (business name, product names, common misspellings). Enables branded vs non-branded analysis in gsc_search_analytics, reports and the dashboard.
| Name | Required | Description | Default |
|---|---|---|---|
| terms | Yes |
Changes observed during successful MCP inspections.
v0.1.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the downstream effect (enables branded analysis) but omits core mutation semantics: whether existing terms are replaced or merged, whether the change persists, and any auth/scope requirements. For a write/config tool with zero annotation coverage this is a significant gap.
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?
Two tightly written sentences with the definition front-loaded and the payoff second. Every clause carries information, though the trailing list of consumers ('reports and the dashboard') is slightly padded relative to the single named sibling.
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 one-parameter config tool with no output schema and no annotations, the description covers purpose and effect but not the operational details an agent needs: replace-vs-append behavior, limits, and required preconditions. Adequate minimum viability, but not sufficient for confident mutation.
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, and it partly does by explaining what a brand term is (business name, products, misspellings). However, it never mentions the operative constraints already in the schema (max 30 terms, 1-60 chars each) nor whether the array overwrites prior terms, leaving the parameter's update behavior undocumented.
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?
States a specific verb (Set) and resource (brand terms), then defines the concept inline ('words that make a query branded') with concrete examples. It also distinguishes itself from siblings by naming gsc_search_analytics as the consumer of this configuration.
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?
Usage is implied rather than stated: the agent can infer this should be called to configure branded vs non-branded segmentation before running analysis. There is no explicit when-to-use, no prerequisite (site must be connected/selected?), and no mention of alternatives or when not to call it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.