List LLM models and prices

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

No annotations exist, so the description carries the full burden; it does disclose the substantive output contract (three price categories, USD, per-million-token normalization) which is the key behavioral detail. It stops short of stating read-only nature explicitly, data freshness, or whether the result is a static snapshot.

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?

A single well-formed sentence with no filler. The resource and provider scope are front-loaded and the price detail follows, so scanning yields the essentials immediately.

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 simple two-filter read tool with no output schema and no annotations, the description covers what is returned well enough to invoke correctly. The remaining gap is the unexplained 'tier' filter, which an agent would have to inspect the schema for and would still not fully understand.

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 must compensate. It partially covers the provider filter by naming the OpenAI/Anthropic/Google enum values, but the 'tier' parameter (flagship/mid/small) is never mentioned or explained, leaving half the filtering surface undocumented anywhere.

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 opens with a specific verb ('List') and a concrete resource ('current OpenAI, Anthropic and Google API models'), then enumerates the price dimensions returned (standard, cached-input, batch) in USD per million tokens. This makes the catalog-listing nature unambiguous versus siblings like get_model_price or estimate_cost, though no sibling is named directly.

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?

Usage is only implied: an agent can infer this is the browse/discovery tool for the model catalog. There is no explicit statement of when to prefer this over get_model_price, compare_models, or estimate_cost, and no prerequisites or exclusions are given.

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