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

Annotations already cover the read-only and closed-world nature of the tool, and an output schema exists. The description adds useful behavioral context by stating that the response includes ready-made problems, the data each needs, and an example, which helps an agent understand the catalog's role.

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 a single, efficient sentence with no wasted words. The key information—accepted problems, required data, and an example—is front-loaded.

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 zero-parameter, read-only catalog tool with an output schema and safety annotations, the description provides enough context: what the tool returns and how it relates to quantum_optimize. It could further clarify the intended discovery workflow, but no critical information is missing.

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 score is 4. The description does not need to explain input parameters, and it appropriately focuses on the information the tool provides.

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 identifies the resource: the ready-made problems accepted by quantum_optimize, plus the data each requires and an example. It distinguishes itself from the sibling tool by naming quantum_optimize, though it relies on the title for an explicit action verb like 'list'.

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 implies the tool should be used to discover accepted problem types and their required inputs before invoking quantum_optimize. It provides clear context but does not explicitly state when-not-to-use it or compare it to other siblings such as quantum_status or quantum_verify.

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