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

Annotations already establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuine behavioral value by enumerating what the response contains (test scores, rank, memory per quantization, smallest GPU/Mac, decision accuracy, Q4 Mac run), which matters because no output schema exists.

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?

It is front-loaded with the usage condition, then moves to return contents and argument examples in two dense sentences. Every clause carries information, though the return-value enumeration is long and slightly list-like.

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?

With no output schema, the description fully compensates by describing the return payload, and it clarifies the single input format. For a one-parameter, read-only lookup tool, nothing an agent needs to call it correctly is missing.

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 coverage is 100% and the single parameter is already documented, so baseline would be 3. The description adds format guidance beyond the schema — 'Accepts a name or slug' plus concrete examples like 'qwen3.5 9b' and 'gpt-oss-20b' — which helps an agent format the argument.

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 states a specific verb+resource with explicit scope: 'a specific open-weight model' and enumerates the question types it answers (quality, benchmark scores, VRAM/memory, quantization choice). It implicitly contrasts with siblings like find_models (search) and compare_models (multi-model) by scoping to a single model.

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?

'Use this whenever the user asks about a specific open-weight model' gives clear triggering context and lists concrete user intents. It never names the alternatives (find_models, compare_models) or states when not to use it, which keeps it short of a 5.

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