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

Annotations already declare readOnly, idempotent, and closed-world, so the safety profile is covered. The description adds genuinely useful context beyond that: it explains cursor-based pagination and states a licensing/attribution obligation (CC BY 4.0, Source: Aleva), which an agent could not infer from structured fields.

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 purpose is front-loaded and every clause carries content (pagination, fields, attribution) with no filler. The dense comma-spliced field enumeration is slightly hard to scan but does not waste space.

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 correctly compensates by detailing the returned fields. Combined with pagination and attribution notes, it is nearly complete for a read-only query tool, though filter behavior and record volume remain unstated.

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 description coverage is 100%, so all 8 input parameters are already documented in the schema. The description adds output-field semantics but no meaning for input parameters such as tld/domain/month filters, so the 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 names a specific resource (llms.txt records in Sweden) and enumerates the returned fields, so an agent knows exactly what dataset it retrieves. It does not, however, distinguish itself from siblings like web_agents, web_ai_stance, or web_apis, which all sound like web-crawl-derived datasets.

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?

There is no explicit guidance on when to use this tool versus the many other web_* tools, nor any prerequisites or exclusions. Pagination is mentioned, but that is invocation mechanics rather than selection guidance.

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