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

Annotations already establish read-only, idempotent behavior, and the description adds useful behavioral context beyond that: it states the output is the current content as plain text and gives the semantic purpose of the resource. It does not address error cases or content size, but for a simple read-only fetch the description covers the key behavioral traits.

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 short, front-loaded sentences contain no filler. The first sentence states the action and output format, and the second adds useful context about why the resource matters for AI agents.

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 parameterless, read-only tool with annotations covering safety, the description is largely complete: it names the resource, the return format, and the content's purpose. The only meaningful gap is not explaining how this relates to the sibling get_llms_full_txt, but that is more a differentiation concern than a completeness failure.

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 has zero parameters, so the input schema fully covers the parameter space and the description cannot add parameter-level meaning. The baseline of 4 applies because no parameter documentation is needed.

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 uses a specific verb ('Devuelve'), names the exact resource ('/llms.txt de VamosAlicante'), and specifies the output format ('texto plano'). It clearly states what the tool does, but it does not explicitly distinguish itself from the sibling tool get_llms_full_txt, so it stops short of full sibling differentiation.

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 context by noting that the resource contains instructions and resources for AI agents, which suggests when an agent might want to retrieve it. However, it gives no explicit guidance on when to choose this tool over alternatives such as get_llms_full_txt or discover_capabilities.

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