stats_app

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

The description mentions it uses the app name from server initialization, indicating a configuration dependency. However, without annotations, it lacks disclosure of side effects, read-only nature, or error conditions (e.g., missing app name).

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 front-loaded with the purpose, followed by a clear bullet list of metric categories. It is concise and avoids unnecessary detail, though the bullet format could be slightly more streamlined.

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 no output schema and no annotations, the description provides a reasonable overview of returned metrics. However, it lacks details on response format, pagination, or error handling, which an agent might need for reliable integration.

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?

No parameters exist, so schema coverage is 100%. The description adds value by enumerating the types of metrics returned, which helps the agent understand what the tool provides despite the empty 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 it returns app-specific statistics like revenue, subscriptions, customer metrics, etc. It distinguishes from general stats tools like stats_get by specifying 'your application', but does not explicitly contrast with siblings.

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 on when to use this tool versus alternatives like stats_get. It does not specify prerequisites or context for use, leaving the agent to infer.

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