label_edit

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does clarify that it's an in-place mutation ('Only the fields you pass are changed') and specifies the index must come from label_list. However, it doesn't disclose whether the operation is reversible, what the return value is, or what happens with invalid indices.

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 tightly written with no wasted words. A brief purpose statement, a usage prerequisite, and illustrative examples all in a compact block. The Args section is formatted clearly and every sentence earns its place.

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?

For a mutation tool with no annotations and no output schema, the description is reasonably complete. It covers all parameters, demonstrates usage, and clarifies partial-update semantics. However, it's a moderately simple tool (4 params, one required), and the potential gap is not disclosing failure behavior or return values, which matters for an editing command.

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%, so the description must compensate. It explains each parameter well: index is a 'Flat label index from label_list', text/new start/new end defaults are 'unchanged', and examples show the exact call patterns. This adds meaningful semantic value beyond the bare schema types.

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?

Purpose is clear: 'Edit an existing label's text and/or timing.' It uses a specific verb (edit) plus resource (label), and the scope is precise — only the fields you pass are changed. The examples with usage patterns (rename vs. move boundary) further clarify the two distinct use cases, and it clearly differentiates from siblings like label_add, label_delete, and label_list.

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 context for when to use it: get the index from label_list first. It shows how to rename versus move a boundary with explicit examples. However, it doesn't explicitly state when NOT to use this tool or name alternatives like label_add_batch or label_regular_intervals, so it lacks explicit exclusions but otherwise provides solid guidance.

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