get_llms_txt

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

With no annotations, description carries full burden but only states it fetches from multiple sources. Does not disclose behavior on missing files, error handling, or source priority. Incomplete for a fetch tool.

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 sentences, front-loaded with core purpose. Could be slightly more efficient by merging explanation of llms.txt into the first sentence, but overall no wasted words.

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?

Output schema exists, so return value explanation is not needed. However, for a documentation-fetch tool, it lacks details on success/failure conditions and multi-source behavior, which is a gap.

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 baseline is 3. Description adds minimal extra meaning beyond schema (e.g., does not clarify how include_full affects behavior or ecosystem differences).

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?

Description clearly states the tool fetches llms.txt documentation for a package and explains what llms.txt is. It distinguishes from sibling tools like create_llms_txt (which writes) and other get tools (which fetch version info).

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?

No guidance on when to use this tool versus alternatives (e.g., when to prefer get_latest_version or rate_package). Lacks context for appropriate usage scenarios or prerequisites.

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