competitor_list

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

With no annotations, the description must carry the full burden of behavioral disclosure. It reveals output fields including creditsCharged, lastError, and cacheSource, which hint at credit usage and caching behavior. However, it does not explicitly state whether the call is read-only, whether it charges credits, or any side effects, leaving ambiguity.

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 succinct, front-loading the core purpose in the first clause, then efficiently enumerating the detail levels and output fields. Every sentence and clause adds value without extraneous fluff, making it exemplary in conciseness.

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 simple list tool with no output schema, the description provides a good level of completeness by specifying the data returned for each verbosity option and noting the dashboard link. The main gap is the ambiguous 'full = raw', which does not sufficiently describe the full object structure, and there is no mention of pagination or error scenarios, though these are less critical for this tool.

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?

Although the schema covers both parameters with descriptions, the tool description adds significant meaning by enumerating the exact fields returned for each detail level (short: id,domain,analysisState; medium: adds source,creditsCharged,...; full: raw). This goes beyond the schema's generic 'verbosity' explanation and helps the agent understand the semantic differences.

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's function with a specific verb ('List') and resource ('the brand's tracked competitors'). It also distinguishes the tool by detailing output levels (short/medium/full) and a unique dashboard link. This differentiates it from sibling tools like competitor_get_overview or competitor_get_comparison.

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 usage by showing the effect of different detail levels (short/medium/full), which guides selection based on desired verbosity. However, it does not explicitly state when to use this tool over alternatives or provide exclusions, so it falls short of a clear usage guideline.

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