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

With no annotations provided, the description carries the disclosure burden. It transparently documents the default filtering behavior, the effect of all=true, and the intended interpretation of zero-coverage results. It does not cover pagination, auth, or response format, but the core behavioral choices are well explained.

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 two sentences and front-loads the most important information: the tool is the entry point, and what it returns. The explanation about all=true earns its place because it prevents a common misinterpretation of zero-coverage results.

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?

There is no output schema and no annotations, so the description must compensate by explaining return values. It states that the result includes how many catalog providers reach each capability and discusses the all=true behavior, but it does not describe the response structure, pagination behavior, sorting, or concrete usage examples. For a six-parameter tool, this leaves some gaps.

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 67%, so the schema already handles q, all, context, and industry. The description adds meaning for the all parameter by explaining default behavior and reframing zero-coverage data as a real answer. It does not add anything for page, limit, q, context, or industry beyond what the schema provides.

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 identifies the resource: a business-capability model with 333 top-level capabilities across 28 industries and counts of catalog providers per capability. The 'START HERE' framing establishes it as an entry-point tool, but it does not explicitly differentiate it from related siblings like get_capability or find_industries.

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?

It provides clear context for when to use the tool: as the starting point for 'what can a business do with these APIs.' It also explains the default vs. all=true choice, including why zero-coverage capabilities are a meaningful result rather than a gap. It does not mention alternative tools or exclusions, so the guidance is contextual rather than comparative.

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