Score a brand name across 5 dimensions: domain availability, trademark risk, social handle availability, SEO strength, and AI findability (GEO score). Returns a composite score 0-100 with detailed sub-scores and a verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The candidate brand name to score | |
| tlds | No | Optional TLDs to check (defaults to com, io, ai, app, dev, co) |
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?
No annotations provided, so the description carries full burden. It lists the five dimensions but does not disclose behavioral traits like data sources, computational cost, or side effects. The mention of 'AI findability (GEO score)' hints at methodology but lacks detail. Adequate but not rich.
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, well-structured sentence that concisely conveys the core function, output format, and dimensions. No redundant words; front-loaded with key action.
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 tool with 2 parameters and no output schema, the description adequately explains the return value (composite score 0-100 with sub-scores and verdict). It mentions all five dimensions, though slightly more detail on sub-scores could help. Overall complete for its complexity.
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 100%: both parameters have detailed descriptions. The tool description adds the purpose of scoring but does not enhance parameter meaning beyond the schema. Baseline 3 applies as schema does the heavy lifting.
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 scores a brand name across 5 specified dimensions and returns a composite score 0-100 with sub-scores and verdict. The verb 'Score' and resource 'brand name' are specific, and the composite nature distinguishes it from sibling tools that check individual aspects.
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 comprehensive brand name evaluation, but does not explicitly state when to use it versus the sibling tools (e.g., for overall scoring vs. individual checks). No exclusions or alternatives are mentioned, but the context is clear enough.
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.
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.
