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

Beyond the readOnlyHint annotation, the description reveals important behavior: not_supported is expected for non-Naver platforms, results mirror the same grammar used by the publisher, and topic-specific sections include silent-failure mistakes. This gives the agent realistic expectations about outputs and errors.

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 moderately long but every sentence earns its place: purpose, exclusion, when to call, topic behavior, and source authority. It is front-loaded with the key purpose and scoping, and the later examples are illustrative, not filler.

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 read-only lookup tool with three parameters and no output schema, the description is complete. It covers supported platform, query behavior, default behavior, result provenance, and a non-error condition. An agent has enough to decide when to call it and what to expect.

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%, so the baseline is 3, but the description adds meaning beyond the schema: it explains what happens without a topic vs. with one, and that returned values come from the grammar the publisher validates against. This complements rather than repeats the schema's parameter descriptions.

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: describe the body markup grammar for a channel, specifically Naver Blog. It distinguishes scope by noting every other platform returns not_supported, and ties the tool to the publish workflow rather than other sibling operations.

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?

Usage guidance is explicit: call this before writing a Naver Blog body for the first time, or when basic formatting doesn't cover a need. It also tells the agent not to treat not_supported as an error and clarifies that other platforms don't support this lookup.

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