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

Annotations indicate a safe, read-only, idempotent operation. The description adds valuable context: 'No API key needed' and the scope of models compared (30+ cloud, 12+ local). It also discloses that results are ranked with cost simulation, which is helpful beyond the annotations.

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 primary purpose, then provides essential details and a key differentiator ('No API key needed'). Every sentence contributes value with no redundancy.

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 tool's moderate complexity (8 optional params, output schema, clear annotations), the description covers the main use case, comparison dimensions, output format, and access requirements. The output schema also exists, so return values need not be detailed in the description.

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%, so the baseline of 3 applies. The description mentions relevant dimensions (cost, speed, VRAM) that relate to parameters like max_budget, tokens_per_day, and vram_gb, but it does not add significant new meaning beyond the detailed schema descriptions.

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 clearly states the tool's purpose: 'Find the best LLM for a given use case.' It specifies comparison criteria (cost, speed, benchmarks, features, VRAM) and output type (ranked recommendations with cost simulation), distinguishing it from generic comparison tools like compare_models or list_llm_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 gives clear context: use this tool when you need to select an LLM for a specific use case. It does not explicitly mention alternatives or state when not to use it, but the context is sufficiently clear to guide an agent.

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