Recommend analysis methods

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

Annotations already declare this is a read-only, idempotent, non-destructive, closed-world operation, so the safety profile is covered. The description adds value beyond that by disclosing the return shape: candidate methods with applicability conditions, key assumptions, R/Python packages, and pointers to library sections. It does not discuss limits (e.g., the default of 3 results) beyond what the schema states.

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 definition is a single dense paragraph but is well front-loaded: purpose and task families come first, input requirements next, return contents last. Every clause adds a distinct capability, though the length borders on an enumeration dump rather than tight prose.

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?

With no output schema, the description compensates by describing what is returned (methods, applicability, assumptions, packages, library sections). Inputs are fully documented in the schema, and the annotations carry the safety profile, so nothing an agent needs to invoke this correctly is missing.

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 coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema by specifying the content the 'question' parameter should carry (data type, assignment mechanism, outcome) rather than only the schema's single example. It also implicitly ties the 'language' parameter to the packages returned.

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 states a specific verb (recommend) and resource (statistical, econometric, causal inference, or ML methods) and enumerates the concrete task families it covers, from test selection to power analysis to predictive modeling. An agent can tell instantly that this tool maps a research question to candidate methods, distinct from the search/fetch/cite siblings.

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?

It gives clear when-to-use coverage by enumerating the triggering scenarios ('which statistical test or model fits the data', 'how to estimate a causal or treatment effect', 'how to analyze an A/B test'), and it specifies what the caller must describe (question, data structure, treatment assignment, outcome). It names no exclusions or alternative tools, but the sibling set contains no competing recommender, so context is otherwise unambiguous.

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