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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds behavior beyond that: results are ordered by name and each item exposes a slug — useful because no output schema exists. It stops short of saying how large the list is or whether it is paginated/cacheable, so not a 5.

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?

Three short sentences with zero waste: trigger first, return shape second, linkage to the follow-on tool third. Every sentence carries distinct information and the most decision-relevant clause is front-loaded.

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?

With no output schema, the description must describe the return value and does: a name-ordered list of markets, each with a slug consumable by get_market_landscape. Combined with annotations covering read-only behavior and a zero-parameter schema, nothing an agent needs to call it correctly is missing.

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?

The tool takes zero parameters, so the baseline is 4 and the description correctly avoids inventing any input semantics. Nothing is promised about arguments that could mislead an agent.

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?

Names a specific verb and resource (list the markets IndustryLens tracks), scopes it to B2B SaaS categories, and explicitly distinguishes it from the sibling get_market_landscape, which it points to for the full map. An agent can tell exactly what it returns and what it does not.

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?

Opens with an explicit trigger ('Use when the user wants to know which markets... or needs a slug for get_market_landscape'), naming both the intent and the downstream dependency. That is a clear when-to-use plus a named alternative for the full category map, so the routing decision needs no inference.

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