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

Annotations already declare readOnlyHint=true and destructiveHint=false, establishing the safe read-only nature. The description adds valuable context beyond that: it fetches the page, extracts content, and emits a single text blob rather than writing to disk. It also discloses that output is in standard llms.txt format. This supplements annotations without contradicting them.

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 tightly written in three sentences: first states the purpose and audience, second explains the mechanics and output, third lists concrete use cases. Every sentence contributes new information, with no filler or redundancy. Essential details (standard format, single text blob) are 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?

Despite having no output schema, the description clearly explains the return value as a single text blob in standard llms.txt markdown, which is essential. It also covers input (any URL) and constraints (max_links via schema). It does not detail error behavior or handling of complex/SPA sites, but for a simple read-only generation tool with strong annotations, this is largely sufficient.

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% for both parameters (url and max_links), so the schema already documents their meaning fully. The description adds no additional semantic detail about the parameters beyond what the schema provides (e.g., it does not explain edge cases, defaults, or constraints beyond the schema's own text). Baseline 3 is appropriate given the schema's completeness.

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 job: generating a production-ready llms.txt file for any URL for AI crawler indexing. It names the exact resource (llms.txt), the process (fetches page, extracts title/description/key links, emits markdown), and the target users (AI crawlers like ChatGPT, Claude, Perplexity). This specificity distinguishes it from sibling tools, most of which focus on research or market analysis.

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 provides explicit use cases: getting a client's site indexed, drafting llms.txt for your own project, or auditing a competitor's AI visibility. This gives clear context for when to invoke the tool. However, it does not explicitly name alternative tools to use in other scenarios, leaving a small gap in when-not-to-use guidance.

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