Evaluate a brand name for AI findability (GEO score). Assesses entity collision, semantic distinctiveness, corpus saturation, and phonetic clarity.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The brand name to evaluate |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It clearly states the tool evaluates multiple dimensions (entity collision, semantic distinctiveness, etc.), indicating a read-only analysis. However, it does not mention any potential side effects, authorization needs, or rate limits.
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 a single, focused sentence that immediately conveys the tool's purpose and scope, with no extraneous information. It is front-loaded and efficient.
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?
Given the single parameter and absence of output schema or annotations, the description provides adequate context about what the tool evaluates. However, it lacks information about the output format, such as whether it returns a numerical score or qualitative categories.
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 schema has 100% coverage with a basic description of 'name'. The tool description adds significant meaning by explaining that the evaluation includes entity collision, semantic distinctiveness, corpus saturation, and phonetic clarity, which goes beyond the schema's parameter description.
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 explicitly states 'Evaluate a brand name for AI findability (GEO score)' and lists the specific aspects assessed (entity collision, semantic distinctiveness, corpus saturation, phonetic clarity), clearly differentiating from sibling tools like check_domain or score_name.
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 use when needing a GEO score for a brand name, but it does not provide explicit guidance on when to use this tool versus siblings (e.g., check_domain, check_social) or when not to use it.
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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Each tool targets a distinct aspect of brand name evaluation: domain availability, GEO score, social handles, trademark conflicts, and overall scoring. There is no overlap in purpose.
Four of five tools follow a consistent 'check_' prefix pattern, but 'score_name' breaks this convention with a different verb. The pattern is otherwise clear.
With 5 tools, the set is well-scoped for a brand name evaluation service. It covers individual checks and a composite scoring function without redundancy.
The tools cover key evaluation dimensions (domain, trademark, social, AI findability, and composite score). Minor gaps might include language-specific checks or URL verification, but core workflows are supported.
