label_cue_across_sequences

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

With no annotations provided, the description carries full responsibility for disclosing behavior. It clearly states it iterates over sequences and labels a cue, and specifies the return type (a dict with 'commands_sent', 'count', and 'summary'). However, it does not mention whether labeling overwrites existing labels or if there are side effects.

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 relatively concise, front-loaded with the main action, and uses a logical structure (description, iteration detail, args, returns, examples). However, the docstring-style re-statement of args is somewhat redundant given the schema. Still, it is not overly verbose.

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 simplicity (4 required parameters, no nested objects, no output schema), the description provides enough context: it explains the iteration, parameter semantics, and return value. It is complete for a basic labeling operation, though it could benefit from edge-case notes (e.g., if cue_id doesn't exist).

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?

The schema has 0% description coverage, so the description must compensate. It lists and briefly explains each parameter (e.g., 'cue_id: Cue number to label (e.g., 1, 0.5)'), but lacks details on accepted ranges, format constraints, or default values. The examples provide context, but the explanation is minimal.

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 tool's purpose: 'Label a cue across a range of sequences.' It specifies the action (label), resource (cue), and scope (range of sequences), distinguishing it from siblings like 'label_macro_tool' or 'label_sequence_cue'.

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 explains the iterative labeling behavior but does not provide explicit guidance on when to use this tool versus alternatives like 'label_sequence_cue' (for a single sequence) or 'appearance_cue_across_sequences' (for appearance). It lacks any 'when not to use' or conditional context.

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