label_clades

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

No annotations are provided, so the description carries the burden. It mentions that labels are written to a column (indicating mutation) and partitioning occurs at a given depth. However, it does not disclose default behavior when depth is null, constraints on input, or potential side effects beyond the mentioned key.

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 a single sentence that efficiently conveys the main action and side effect with no wasted words. Every part is essential.

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's moderate complexity (tree partitioning with 4 parameters) and the existence of an output schema, the description is minimally complete but lacks crucial parameter details. It provides the core action but not enough context for effective use without additional inference.

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 0%, meaning no parameter descriptions exist in the JSON schema. The description does not provide any additional meaning or explanation for any of the 4 parameters (depth, tree_key, key_added, dataset_id), leaving the agent to infer from names alone.

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 (partition a tree into clades at a given depth) and the outcome (labels written to obs[key_added]). It is specific and distinguishes this tool from sibling tools that perform other tree operations like computing parsimony or plotting.

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 provides no guidance on when to use this tool versus alternatives, no when-not-to-use conditions, and no mention of prerequisites or context. It simply states what the tool does.

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