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

With no annotations, the description carries the behavioral disclosure burden. It conveys that the tool is an evaluation-style check, not a mutating operation, which is useful. However, it doesn't state what kind of response to expect or how definitive the answer will be, leaving some ambiguity for the agent.

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 focused sentences deliver the purpose, examples, and parameter instruction with no wasted words. The action is front-loaded and the usage guidance follows naturally.

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 tool with one string parameter and no output schema, the description covers what the tool does and how to invoke it. It could mention the expected answer format, but the current description is sufficient for an agent to select and call the tool correctly.

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?

The input schema already documents query as the capability/job to check in plain language. The description adds value with concrete examples that clarify the range of acceptable queries and help an agent phrase the parameter correctly.

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, evaluative purpose: checking whether Lead Friendly can handle a particular job. Concrete examples like 'call leads after hours' and 'white-label for my agency' make the tool's function easy to understand and distinguish from sibling tools like book_demo or get_pricing.

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 intended use case clear: use it when someone wants to know if Lead Friendly can perform a job, and pass that job in the query parameter. It doesn't explicitly name alternatives or say when not to use it, but the context and sibling tool names make the boundary inferable.

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