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

Annotations already declare the operation read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond that: it assumes ~4-bit quantization and 8K context, ranks fitting models first and largest first, and reports max context per model. This meaningfully informs the agent about how results are produced.

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 two sentences, front-loaded with the core purpose, and contains no filler. Every clause contributes either the behavior, the assumptions, or the trigger conditions for use.

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 read-only tool with a fully documented schema, the description covers what the tool does, when to use it, and the shape of the output (ordered list with max context). It does not specify the exact return format, but the absence of an output schema is mitigated by the description's clarity about ranking and included fields.

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 parameters are already fully documented. The description adds high-level context by mentioning GPU/Apple Silicon and 'N GB', which maps to gpu and mac_ram_gb, but it does not add detail beyond the schema. Baseline 3 is appropriate.

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 ('Rank') and a clear resource ('popular local LLMs fit on a given GPU or Apple Silicon Mac'), and it states the ranking criteria. It is clearly differentiated from the likely sibling tools by focusing on hardware-to-model matching rather than checking a specific fit or listing all supported 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 explicitly says when to use the tool ('Use when a user asks...') and includes concrete example queries. It does not explicitly mention when not to use it or name sibling alternatives, so it stops short of full exclusion guidance.

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