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

With no annotations provided, the description carries the full burden, and it does well by disclosing the computation basis and the full set of possible verdicts (EXCELLENT through UNKNOWN) with plain-language reasons. As a read-only analysis tool it implies no side effects, and the absence of mutation behavior is reasonably inferable from 'returns' and 'verdicts'.

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 sentences, every one earning its place: purpose/first sentence, output detail/second sentence, and prerequisite/third sentence. No filler or repetition of schema fields.

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?

Although there is no output schema, the description compensates by enumerating the verdict values and stating that a best recommendation plus reasons will be returned. Inputs, defaults, and hardware-id sourcing are all covered via schema and description, making this complete for an agent to select and invoke the tool.

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 100%, so the schema already documents each parameter clearly. The description adds extra meaning by linking the context and kv parameters to the measured/computed analysis, and by telling the agent where hardware ids come from (list_hardware), which is beyond what the schema states.

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 clear, specific question—'Which tracked models run on a given GPU or Mac'—and goes on to name the exact analysis (GGUF weights, context cache, runtime overhead vs. usable memory). This clearly differentiates find_fit from siblings like gpu_prices, list_hardware, and trending_models.

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 makes the core use case obvious: when a user has a target GPU or Mac and wants to know which models fit. It also gives a concrete prerequisite by pointing to list_hardware for obtaining hardware ids, but it does not explicitly state when to avoid this tool or prefer gpu_prices/trending_models.

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