get_most_reliable_free_model

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

There are no annotations, so the description carries the full burden. It reveals the selection logic ('highest measured 7-day uptime' and 'currently answering'), but it does not disclose the return format, behavior when no models are available, or whether the uptime measurement involves live checks or cached data.

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 sentence that front-loads the verb and the key criterion. Every phrase earns its place; there is no redundant filler.

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 zero-parameter, no-output-schema getter, the description covers the essential semantics: what is returned and how it is selected. It does not specify the exact response shape or failure behavior, but those are minor gaps for a simple lookup tool and no annotations exist to complicate.

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, so there is no parameter meaning for the description to add; baseline 4 applies. The description's reference to 'currently answering' contextualizes the implicit dynamic input state rather than any schema parameter.

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 verb ('Get'), a specific resource ('free LLM API'), and the precise selection criterion ('highest measured 7-day uptime among those currently answering'). This clearly distinguishes it from the sibling get_fastest_free_model, which optimizes speed rather than reliability.

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 this tool—when you need the most reliable currently available free model—but it never names an alternative or states when to prefer another sibling. There is no explicit exclusion or when-not guidance.

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