multi_languages

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

With no annotations provided, the description carries the full burden of behavioral disclosure but fails completely. It doesn't indicate whether this is a read or write operation, what kind of output to expect, any performance characteristics, or behavioral constraints. The phrase 'Best model' suggests some kind of evaluation or selection, but no actual behavioral information is provided.

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 extremely concise (4 words) but suffers from under-specification rather than effective brevity. While it's front-loaded with the core claim ('Best model for multi languages'), it lacks the necessary substance to be truly helpful. The conciseness comes at the cost of meaningful information, making this more sparse than efficiently structured.

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 annotations, and no output schema, the description is incomplete for understanding what the tool actually does. While zero parameters reduces complexity, the description fails to explain the tool's function, behavior, or output. The agent cannot determine what 'Best model for multi languages' means operationally - is this a recommendation system, a translation tool, or something else entirely?

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 0 parameters with 100% schema description coverage, so the baseline score is 4. The description doesn't need to compensate for any parameter documentation gaps since there are no parameters to document. The description's mention of 'multi languages' could be seen as providing context about what the tool operates on, but this is minimal value given the zero-parameter nature.

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 'Best model for multi languages' is vague and tautological - it essentially restates the tool name 'multi_languages' with minimal additional information. It doesn't specify what action the tool performs (is it selecting a model? evaluating models? translating?), nor does it distinguish from sibling tools like 'basic_model' or 'easy_use_humanizer'. The description lacks a clear verb+resource combination that would indicate the tool's function.

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 provides absolutely no guidance on when to use this tool versus alternatives. There's no mention of context, prerequisites, or comparison to sibling tools. The agent receives no information about appropriate use cases, making it impossible to determine when this tool should be selected over 'basic_model' or 'easy_use_humanizer'.

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