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

With no annotations, the description carries the full burden and does disclose useful behavior: it is free and triggers no model call, and 'Returns' implies a non-mutating read. It does not state auth requirements, whether the catalog is cached/stale, or pagination behavior, so it falls short of fully covering the missing annotation profile.

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, zero waste, and the core capability is front-loaded before the usage guidance. Every clause 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?

No output schema exists, so the description correctly compensates by summarizing the returned structure and noting the free/no-model-call cost profile. For a parameterless read tool, an agent has everything needed to call it and route its result.

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 takes zero parameters, so the baseline is 4. The description still adds value by clarifying what schema_id means and where it should be passed (diagnose_response), which is meaning beyond the empty schema.

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 and resource ('Returns Quantabble's coverage and full topic catalog') and enumerates the returned fields (schema_id, course, unit, topic number, title). This distinguishes it clearly from the sibling diagnose_response, which consumes rather than supplies the schema_id.

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 two concrete when-to-use conditions (verify a topic is in scope; look up the schema_id to pass to diagnose_response when the student's topic is already known) and explicitly names the sibling tool to hand off to. Nothing about selection is left to inference.

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