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guifreballester-seranking

SE Ranking SEO Data API MCP Server

AIO Discover Brand

domainAioDiscoverBrand

Find brand names cited in Google AI Overviews for any domain, subdomain, or URL. Specify a country code and target to discover which brands are mentioned in AI search results.

Instructions

Fetch AI Overviews brand names (v1/domain/aio/discover-brand)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoThe scope of the analysis. Can be base_domain (domain and all subdomains), domain (specific host), or url (exact URL).base_domain
sourceYesThe alpha-2 country code for the regional database. Example: us
targetYesThe domain, subdomain, or URL to analyze. Example: seranking.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden of behavioral disclosure. It only states what data is fetched and does not explain return structure, limitations, regional behavior, or any side effects. This is minimal but not misleading.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler. It wastes no words and immediately identifies the operation and resource.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema or annotations, the description leaves important context missing: what the returned brand names look like, whether results are paginated, and how this differs from the related AIO sibling tools. The high schema coverage helps with parameters but not with overall call semantics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters and their meanings. The description adds little beyond naming the endpoint, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Fetch') and resource ('AI Overviews brand names'), and the endpoint string reinforces the purpose. It does not explicitly differentiate from sibling tools like domainAioKeywordsByBrand, but the verb and resource are specific enough for basic identification.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus its siblings. There is no mention of scenarios, prerequisites, or alternative tools, leaving the agent to infer usage from the name and schema alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.