glm_5_compare_model_recommendations

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

No annotations are present, so the description must fully convey behavioral traits. It only states 'compare' without explaining side effects, permissions, or output behavior, leaving significant ambiguity about what the tool actually does.

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, clear sentence with no unnecessary words. It is well-structured and easy to read.

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 lack of output schema and the minimal parameter information, the description is insufficient. It does not specify what the comparison produces or how the 'context' parameter is used, leaving the tool's overall functionality underdefined.

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 description provides no information about the parameters 'question' and 'context'. The schema coverage is 0%, and the description fails to explain how the parameters relate to the 'reasoning-oriented' and 'knowledge-oriented' answers mentioned, adding no value beyond the field names.

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 verb 'compare' and the resources 'reasoning-oriented GLM answer' and 'knowledge-oriented answer', making the purpose specific and distinguishable from sibling tools that focus on consultation or routing.

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?

No guidance is provided on when to use this tool versus alternatives such as glm_5_consult_knowledge or glm_5_query_reasoning. There is no mention of prerequisites or when not to use it.

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