Usage Stats

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

Annotations already disclose readOnly=true, idempotent=true, and destructive=false. The description adds meaningful behavioral context by specifying that counts are since deployment, gateway-scoped, and include a top-5 breakdown. This complements the annotations without contradicting them.

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?

Three short sentences: what it is, what it returns, and how to call it. Every sentence earns its place, and the main purpose is front-loaded. No filler or repetition.

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 zero-parameter, read-only tool with an output schema available, the description covers the return fields, scope, and invocation format. An agent has everything it needs to select and call this 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 tool has zero parameters and 100% schema description coverage, so the baseline is 4 under the rubric. The description reinforces this with an explicit no-argument example, though no parameter explanation is needed.

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 resource ('tool-call counters') and a clear scope ('since deployment', 'recorded by this gateway instance'). It also enumerates the exact response fields, so an agent can distinguish this from sibling tools like registry_stats or list_markets without opening schemas.

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 phrase '(traction/uptime metrics)' clearly identifies when this tool is relevant, and the example usage_stats() shows there are no arguments to supply. It does not explicitly name sibling alternatives or exclusions, but the context is sufficient for an agent to decide to call it.

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