fetch_llms_txt

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

No annotations are provided, so the description carries the full burden. It mentions that llms.txt files can compile lists of URLs and suggests following references, which adds some behavioral context. However, it lacks details on error handling, rate limits, authentication needs, or what happens if URLs are invalid, leaving significant gaps for a tool that fetches external content.

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 appropriately sized with two sentences that convey key information: the core function and a usage tip about following links. It's front-loaded with the main purpose, though the second sentence could be more tightly integrated to avoid slight redundancy.

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?

Given no annotations and no output schema, the description is incomplete for a tool that fetches external URLs. It lacks details on return values (e.g., content format, error responses), authentication, rate limits, or how to handle multiple URLs, making it inadequate for safe and effective use by an AI agent.

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 schema description coverage is 100%, with the parameter 'input' well-documented as accepting URL strings, objects, or arrays. The description doesn't add any parameter-specific semantics beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage without extra value.

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 action ('fetches the content') and resource ('one or more llms.txt URLs'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_llms_txt_sources' which might list URLs rather than fetch content, leaving some ambiguity.

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 by explaining that llms.txt files may contain links to other files, suggesting this tool is for following such references. However, it doesn't explicitly state when to use this tool versus alternatives like 'fetch_openapi_spec' or 'list_llms_txt_sources', providing only contextual hints rather than clear guidelines.

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