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

Annotations are absent, so the description carries the full burden. It discloses a critical side effect—the x402 payment requirement and exact cost. However, it does not state whether the operation is read-only or whether it modifies any state, which is a notable gap for a recommendation tool with no annotation support.

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?

Two sentences with no fluff: the first states the action and objective, the second states the mandatory payment. Information is front-loaded and every word earns its place.

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?

The tool has three parameters, an output schema, and zero annotations. The description covers the purpose and payment but omits parameter semantics and usage constraints. An agent would still be unclear about how to populate require_tool_calling or required_context_tokens effectively, making the overall context incomplete.

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 0%, and the description provides no explanation of the parameters. The tool name and general narrative imply task_description, but require_tool_calling and required_context_tokens are left unexplained. The description adds minimal value over the bare schema.

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 verb ('Recommend'), the resource ('which LLM'), and the scope ('for one specific task'), and adds the balancing objective (cost vs. validated quality benchmark). With no sibling tools, there is no risk of confusion, and the purpose is unambiguous.

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 conveys a clear context of use: when you need a model recommendation for a single task, weighing cost against quality. It does not provide explicit exclusions or alternative tools, but with no siblings listed, the use case is sufficiently defined for an agent to know when to invoke it.

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