optimizer_list_classes

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

No annotations are provided, so the description carries full burden. It discloses the tool lists classes with counts, which is appropriate for a read-only operation. However, it does not explicitly state that it's non-destructive or idempotent, but given the simple nature, a score of 3 is adequate.

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, both essential. First sentence states action and content, second provides context. No filler words, perfect front-loading.

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?

Given no output schema, the description does not detail return format (e.g., JSON structure). However, it sufficiently describes what is returned (classes with counts) and the purpose, which is adequate for a simple listing tool.

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?

There are zero parameters, and schema coverage is 100% (empty schema). The description adds meaning by specifying the output content (classes with technique counts) and the taxonomy purpose, going beyond the 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?

The description clearly uses the verb 'list' with the resource 'algorithmic problem classes' and adds detail about including technique counts. This distinguishes it from sibling tools like 'optimizer_list_techniques' and 'optimizer_match_problem'.

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 second sentence explicitly states the purpose as 'The taxonomy to orient on before matching a problem,' giving clear usage context. While it does not explicitly state when not to use, the guidance is strong.

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