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

The phrase 'Values discarded' discloses that values are ignored and the operation has no value-dependent behavior. However, with no annotations to cover safety or side effects, the description still omits return format, error behavior on malformed JSON, and whether the count is recursive.

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 short sentences deliver the core operation and a behavioral caveat with no filler. The key information is front-loaded and each sentence 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?

For a simple counting tool, the description is nearly sufficient, but it omits whether the result is an integer count, whether nested keys are counted, and how errors like non-object input are handled. Given the absence of an output schema and annotations, some of that burden falls on 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?

The schema has 100% parameter descriptions, so the baseline is met, but the tool description itself does not explain which parameter to populate or that the whole input object is the thing being counted. The mix of unrelated parameters such as url, city, and host makes this ambiguity material.

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 concrete operation: count keys in a JSON object, and clarifies that values are ignored. This distinguishes it from validation/shape tools, though it leaves ambiguous whether the count is top-level only and which input parameter carries the object.

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?

No guidance is given about when to use this tool versus siblings like validate-json or domain-shape. There are no conditions, exclusions, or named alternatives, so an agent must infer applicability from the name and brief description.

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