list_supported_languages

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

With no annotations, the description carries the burden. It discloses the output shape (Steam codes and labels) and purpose, implying a read-only operation. It doesn't mention pagination or sorting, but for a static list these are not critical.

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 concise sentences with no redundancy. The first sentence states what is listed; the second explains how to use the result. Every word 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?

For a zero-parameter, no-output-schema tool, the description is fully sufficient. It tells the agent what it returns, with an example, and how to apply that output. Nothing essential 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 has zero parameters, so description adds no parameter-specific meaning. However, it clarifies that the returned codes are intended for the 'language parameter' of other tools, which is useful context. Baseline 4 for no-param tools is appropriate.

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 uses a specific verb ('List') and clearly identifies the resource: languages for localization with Steam codes and labels. It includes an example code ('schinese') and explains the output structure, making it distinct from sibling data-fetching tools.

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 states how to use the tool's output ('Pass one of these to the language parameter of other tools'), providing clear usage context. It doesn't explicitly state when not to use it, but for a simple enumeration tool this is sufficient.

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