check_brand_visibility

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

With no annotations, the description carries full burden. It explains the simulation and analysis but does not disclose whether the tool is read-only, requires permissions, or has side effects. Lacks mention of rate limits or data freshness.

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 sentences: first states the core purpose, second adds detail on scope and outputs. No redundant words, front-loaded with key information.

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?

The description covers purpose and high-level output, but lacks detail on return format (no output schema provided). Prerequisites like brand existence are implied but not stated. Adequate but not comprehensive.

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 descriptions cover all three parameters (100%). The tool description adds context about the analysis output but does not enhance parameter meaning beyond what the schema already provides. Baseline 3 is appropriate.

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 specifies a clear action ('Check a brand's visibility'), names specific platforms (ChatGPT, Perplexity, Claude, Gemini), and mentions detailed analysis (mention rates, positions, sentiment, competitor landscape). This distinguishes it from siblings like 'check_single_query' and 'compare_brands'.

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 this tool is for assessing brand visibility across multiple AI platforms, but it does not explicitly state when to use it vs. siblings like 'check_single_query' or 'compare_brands'. No exclusion criteria or contextual cues are provided.

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