Classify a study into a design

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds significant behavioral detail: it scores every design, returns matched signals, lists reviewer-expected analyses, and explicitly refuses to guess when no design matches, instead requesting more facts. This is valuable transparency beyond the annotations.

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 front-loaded with the core purpose, followed by instructions, an example, return values, and error handling. Every section earns its place, and the format is scannable for a complex classification tool.

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 lacking an output schema, the description covers the return contract: best match, confidence flag, ranked table, mandatory analyses, and error-handling behavior. Input requirements are explicit, so an agent has everything needed to invoke the tool correctly.

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 both parameters are already documented. The description adds value by specifying exactly what the free-text description should include (cohorts, provenance, outcome variable, validation cohort) and by providing a concrete example input. This exceeds the baseline but is not fully transformative.

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 opens with a specific verb and resource: 'Decide which lesion/stimulation mapping design a described study belongs to.' It clearly distinguishes itself from siblings like lnm_list_designs by stating the tool classifies rather than lists, and it explains what is returned: a ranked list, confidence flag, and mandatory analyses.

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 gives clear when-to-use guidance: pass a free-text description containing cohorts, origins, outcome variable, and validation cohort. It provides an example and explains error-handling behavior when nothing matches. It does not explicitly name alternative sibling tools or state when not to use it, but the context is strong enough for an agent to select it appropriately.

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