Licensing

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

The description adds valuable context beyond annotations by specifying what licensing data is returned (type, expiration, seat count, usage, activation status) and the tool's diagnostic purpose. While annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, the description usefully elaborates on the specific information retrieved and use cases.

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 perfectly concise with two sentences: the first states what the tool does and what data it returns, the second provides usage scenarios. Every word earns its place with zero redundancy or wasted space, and the information is front-loaded effectively.

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 parameterless read-only tool with comprehensive annotations, the description provides excellent context about what licensing information is retrieved and when to use it. The only minor gap is the lack of output schema, but the description compensates by listing the specific data points returned. It's nearly complete for this tool's complexity level.

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?

With 0 parameters and 100% schema description coverage, the baseline would be 4. The description appropriately doesn't discuss parameters since none exist, and instead focuses on the data returned by the tool, which is the correct emphasis for a parameterless query tool.

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 with specific verbs ('get licensing status') and resources ('RAS licensing'), listing concrete data points like license type, expiration date, seat count, usage, and activation status. It distinguishes itself from sibling tools by focusing specifically on licensing information rather than administrators, configuration, performance, or other aspects.

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 provides clear usage contexts ('check license compliance, verify capacity, or diagnose licensing issues'), giving practical scenarios for when to use this tool. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools, which prevents a perfect score.

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