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

Annotations already declare readOnly, idempotent, closed-world behavior, so the bar is lower. The description adds real value beyond them: prices are USD per 1M tokens, estimated_cost_usd reflects the supplied token counts, and output is one row per provider sorted cheapest first.

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?

Three compact sentences, all front-loaded with the resource and ordering before the fallback pointer and cost semantics. Nothing is wasted; the trailing "limit: 1-10." is terse but functional.

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 4-parameter read tool with an output schema, the description covers the input meaning, cost estimation semantics, result ordering, and the name-resolution fallback. An agent can invoke it correctly without opening anything else.

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?

Schema description coverage is 0%, so the description carries the full burden, and it does compensate for most parameters: it explains model with examples, clarifies that input_tokens/output_tokens drive the cost estimate, and gives the 1-10 range for limit. Only the defaults (limit=5, 1M tokens) are left to 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 names the specific resource (inference providers for one LLM) and the ranking criterion (cheapest first), with concrete model examples like "deepseek-v3". The verb is only implied by the noun phrase, but an agent can still tell what it returns and how it is ordered.

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?

It explicitly routes the agent to find_model when the model name is uncertain, which is the main ambiguity for this tool. It doesn't state when NOT to use it (e.g., versus cheapest_gpu), but the model-vs-GPU distinction is implicit in the examples.

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