list_free_models

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

With no annotations, the description carries the full behavioral burden. It does add value by disclosing that the data is 'live measured' and lists the specific metrics returned, giving agents a sense of what to expect. However, it does not mention whether results are sorted, cached, or if there are any rate limits or access considerations—though for a pure list operation these gaps are relatively minor.

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-constructed sentence that front-loads the core action ('List all tracked free LLM APIs') and then packs the relevant return fields into a compact list. There is no filler or repetition—every phrase earns its place.

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 simplicity (no params, no output schema), the description covers what an agent needs to know: the scope of results and the attributes included. It could be slightly more complete by explaining how the list is ordered or how the 'status' is measured, but the absence of parameters and presence of field-list details make it largely sufficient for correct invocation.

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 zero parameters and the schema is trivially 100% covered. The description does not reference any parameters, which is appropriate since there are none. Baseline for 0 params is 4, and the description does not need to add anything further.

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 uses a specific verb ('List') and a clearly defined resource ('all tracked free LLM APIs'), and enumerates the exact fields returned (status, throughput, uptime, tool-calling support). This distinguishes it from siblings like get_fastest_free_model and check_model_status, which target a single model or property.

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 when to use the tool (when an agent needs a full overview of all free models), but it does not explicitly reference alternatives or state when NOT to use it. No guidance is given like 'use get_fastest_free_model when you only need the fastest one,' so the usage context is implied rather than explicit.

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