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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the bar is lower. The description adds meaningful behavior: the listing is exhaustive ('every SANTISMM Lab'), includes formulas/assumptions/citation URLs, and the optional kind restriction changes the result scope. Nothing contradicts the openWorldHint or other annotations.

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?

Two sentences with zero filler. The core action and return contents are front-loaded, followed by usage and filtering guidance. 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?

Low complexity (one optional enum parameter), an output schema, and safety annotations mean the description need not explain return format or side effects. It covers what is listed, the discovery purpose, and when to filter — enough for an agent to invoke it correctly. The only omission, an explicit pointer to get_lab for single-Lab detail, is minor at this complexity.

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 coverage is 100% and the schema description already explains 'Restrict results to one Lab kind. Omit to list every Lab.' The description adds value by mapping the parameter to user phrasing ('when the user specifically asks for a calculator, converter, experiment or educational game'), helping the agent decide when to pass kind. This justifies a step above the baseline.

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?

States a specific verb ('List') and a precise resource ('every SANTISMM Lab'), and enumerates what each listing contains (inputs, outputs, assumptions, formulas, citation URL). The resource-specific naming and the title's four Lab kinds distinguish it clearly from sibling list_* tools and from get_lab.

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?

Gives an explicit use case: 'Use this to discover interactive and machine-readable tools.' It also instructs when to apply the filter — 'filter by kind when the user specifically asks for a calculator, converter, experiment or educational game.' It stops short of naming alternatives or exclusions (e.g., when to prefer get_lab for a single Lab), so it falls just below fully explicit routing.

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