classify_feature_tree

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

Despite no annotations, the description indicates the tool is a 'read-before-write helper', strongly implying no side effects. It describes the inputs (model-info and tree data) and output (classification). Missing details on error handling, but overall clear behavioral intent.

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?

Two concise sentences. The first states the core action, the second elaborates on the output categories and context. Every sentence adds value with no 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?

With a known output schema and 100% parameter coverage, the description adequately covers purpose, inputs, and output categories. The 'read-before-write' context is valuable. Missing a brief note on the output format, but the output schema likely covers that.

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 description need not elaborate on parameters. It mentions using 'model-info and tree data', which aligns with the parameters, but adds no additional semantic nuance beyond the schema.

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 uses the specific verb 'classify' and clearly identifies the resource as the 'active model' and output as a 'feature family'. It lists the possible classification categories, distinguishing this tool from all sibling tools, none of which perform classification.

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 explicitly states 'This is a read-before-write helper for delegation', giving clear guidance on when to use it (before writing, for delegation). As the only classification tool, no alternative is needed, and the context is well-defined.

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