LLM modellerini listele

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

Annotations already declare readOnly, idempotent, and openWorld, and the description adds the data source ('Artificial Analysis') plus the available sort/filter behaviors. However, it does not disclose pagination, freshness/refresh semantics, or rate/usage limitations; the description adds useful but non-essential context.

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?

One front-loaded sentence states the core purpose before moving to filter/sort details, and the details are dense but reasonably organized with semicolons. There is no filler or repetition, though the length makes it slightly dense for quick scanning.

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?

The description establishes the main listing behavior and maps the enum, but with 7 parameters at 0% schema coverage it leaves multiple parameters unexplained, and the output schema cannot compensate for missing invocation semantics. For a list tool with no required params, an agent can call it safely but cannot correctly use max_price_blended, min_intelligence_index, or force_refresh without guessing.

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 burden. It explains creator and enumerates the sort_by options (including index_cost and ttft), but omits limit, descending, force_refresh, max_price_blended, and min_intelligence_index entirely. This leaves most parameters semantically undocumented.

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?

States a specific action and resource ('Lists LLMs on Artificial Analysis') and names the included attributes (price, speed, benchmark indices). The verb and resource distinguish it from siblings like get_model and compare_models, so an agent can recognize it as the bulk-list entry point.

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 implies this is the listing tool compared to get_model, compare_models, or list_providers, but it never states when to prefer it over those alternatives or when not to use it. It provides clear functional context (filter by creator, sort options) but no explicit routing guidance.

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