list_26_systems

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

No annotations are provided, so the description carries full burden. It states the tool returns a list with specific fields but does not disclose behavioral traits like idempotency, safety, or read-only nature. For a simple list, this is acceptable but could be improved.

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 only two sentences, with no wasted words. It front-loads the purpose and provides all necessary information efficiently.

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 the tool has no parameters, no output schema, and sibling tools exist, the description is complete. It explains the content of the list and its intended use. It could optionally mention the output format (e.g., JSON array), but that is not critical.

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 has zero parameters and the schema coverage is 100% (empty schema). Per guidelines, baseline is 4. The description adds value by detailing what each returned entry contains, which goes 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 states the tool returns a canonical list of 26 systems, specifies each entry's fields (slug, English name, Thai name, region, required input fields), and distinguishes it from sibling tools by mentioning 'canonical' and its use with get_deep_reading.

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 explicitly advises 'Use this tool first when a user asks "what systems do you support?"', providing clear context. It does not mention when not to use it, but for a simple list tool this is adequate.

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