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

With no annotations provided, the description carries the burden. It discloses that the output differentiates UI and AI tutor coverage, which is a useful behavioral nuance. However, it does not mention any restrictions, output format, or side effects (though none are expected for a read-only tool).

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 three sentences, front-loaded with the primary purpose. The third sentence about not using general knowledge is somewhat tangential but still valuable context for the AI agent, so it 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 simple read-only tool with one optional parameter and a clear schema, the description provides enough context about what is returned (list with separate coverage details) and when to use it. No output schema exists, but the return value is straightforward. Minor gap: doesn't describe the exact shape of 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?

The schema already documents the single 'locale' parameter with 100% coverage, including examples and default behavior. The description adds no additional parameter semantics, so a baseline score of 3 is appropriate.

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 verb 'Returns' and the resource 'list of languages supported by Makuri', and distinguishes from sibling tools by specifying the unique aspect of UI vs AI tutor coverage.

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?

It explicitly instructs when to use: when the user asks which languages are supported or if a specific language is available, and also cautions against using general knowledge for Makuri questions, indicating a preference for this tool.

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