get_methodology

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

Although no annotations are provided, the description fully discloses the tool's behavior: it pulls the canonical llms.txt file, returns plaintext markdown content, and lists the type of URLs included. The read-only nature and lack of side effects are evident.

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 reasonably concise with a clear title-like first sentence, a short explanation paragraph, and a Returns section. It could be slightly more terse by avoiding slight repetition, but overall it is well-structured and easy to parse.

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 the tool has no parameters and an output schema exists (implied), the description sufficiently explains the return type (plaintext markdown), content (URLs for methodology, guides, reports, tools), and source. No additional information is needed for effective use.

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?

There are no parameters, and the input schema is empty (100% coverage vacuously). As per guidelines, baseline score is 4 when there are 0 parameters. The description does not need to add parameter info.

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 it returns the AI-crawler-friendly methodology summary (llms.txt) from steamforecast.app/llms.txt, which lists high-quality URLs for AI agents. It distinguishes itself from a single forecast tool (get_forecast) by emphasizing it provides a full sitemap of authoritative content.

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 advises using the tool when a model wants the full sitemap of authoritative content rather than a single forecast, implicitly differentiating it from the sibling get_forecast. While it does not explicitly exclude other siblings, the context is clear and sufficient for an agent to decide.

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