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

With no annotations provided, the description carries the full transparency burden. It details return format (JSON with matching marks and risk rating), behavior of includeVariations (slower, more lookups), authentication (API key required, paid plan), and cost model (screening allowance, re-screening free). This is a strong disclosure of behavioral traits, though it omits potential error handling 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 three sentences with no wasted words. The first sentence presents the core purpose and data source. The second explains the output and variation option. The third covers legal disclaimer and cost. Information is front-loaded and every sentence adds value. Ideal conciseness for a tool with three parameters.

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 no output schema, the description adequately describes the return value (JSON with matching marks and risk rating). It covers authentication, cost, and a behavioral variant (includeVariations). However, it does not explain error cases (e.g., invalid name, quota exceeded) or what 'matching marks' entails. For a moderately complex tool, this is mostly complete but could be slightly richer.

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 coverage is 100%, so the baseline is 3. The description adds value by explaining that includeVariations triggers 'common spelling variations' and notes it is 'slower, more lookups,' which is not in the schema description. This nuance helps agents weigh performance tradeoffs. The other parameters are well-documented in the schema, and the description's extra context on includeVariations justifies a 4.

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 specifies the verb 'screen' and the resource 'brand name for US trademark conflicts against the USPTO database with similarity analysis.' It distinguishes from sibling tools by highlighting similarity analysis and the USPTO focus, which other tools like namesniper_check likely lack. The scope (US trademarks) and output (risk rating) are explicit, leaving no ambiguity about the tool's function.

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 states the tool is for screening brand names and includes a disclaimer ('not legal advice') and cost implications (API key, allowance). However, it does not directly compare to sibling tools like namesniper_check or namesniper_alternatives, nor does it specify when to prefer this tool over others. The usage context is implied but not explicitly differentiated.

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