label_get_all

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

With no annotations, the description carries the full burden, and it does disclose the key behavioral trait: it returns structured data with specific fields, not raw content blocks. It provides enough transparency for a zero-parameter read operation, though it could mention whether 'all labels' means all in the project or some other scope.

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, tightly worded sentence that immediately states what the tool returns and how it differs from its sibling. There is no redundant or filler content.

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 simple, zero-parameter read tool, the description is largely complete: it names the tool, the output fields, and the distinguishing alternative. The only minor gap is not explicitly defining the scope of 'all labels,' but the sibling comparison and structured-output detail make the tool safely invocable.

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 tool has zero parameters and schema description coverage is 100%, so there is nothing for the description to clarify. The baseline of 4 applies because no parameter ambiguity exists.

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 identifies the operation: 'Get all labels' and specifies the exact output shape (key, text/title, start, end). It also explicitly contrasts itself with sibling label_list, making its purpose distinct even among many label-related tools.

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 names the alternative tool label_list and explains the difference: this tool returns structured data rather than raw MCP content blocks. This gives an agent a clear basis for choosing label_get_all over label_list.

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