board_labels

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

With no annotations, the description carries the full behavioral burden yet says nothing about read-only semantics, the 50-item default/cap, or the fact that 'fields' controls response shape. Only the implicit 'discovering' hints at a safe read; nothing about pagination or rate limits is disclosed.

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?

A single well-formed sentence with the usage trigger front-loaded and no filler. It is efficient but slightly under-specified rather than over-long.

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 3-parameter read tool with no annotations and no output schema, the description should mention the returned shape at least in outline (names, colors, uses) and that results are capped. As written, an agent can call it but cannot predict the response.

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% - each of boardId, limit and fields is documented in-schema, so the baseline is 3. The description adds no extra meaning about parameter behavior or interactions.

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 conveys a clear verb (discovering) and resource (labels available on a board), so an agent knows this returns a board's label set. It does not differentiate from nearby siblings such as label_get or card_labels, which could also surface label data.

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?

'Use when ... before creating or updating cards with labels' gives a concrete trigger context. It stops short of naming an alternative (e.g. label_get for a single label) or stating when not to use it.

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