Lifecycle stats

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

No annotations are provided, so the description carries full responsibility. It explicitly discloses a non-obvious side effect: the call may persist lazy housekeeping ('may first persist lazy expiry/drip housekeeping') even for a read. It also clarifies the timezone basis ('local calendar day on the machine running this server'), adding meaningful behavioral context.

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 concise, consisting of two sentences. The first sentence front-loads the core counting purpose, while the second adds a crucial behavioral caveat. Every phrase adds value, and the README references are appropriate for follow-up details.

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 tool with no parameters and no output schema, the description covers the essential aspects: the data reported (counts per layer, inbox pending), the time window, and the potential write side-effect. It does not detail the return format, but that is not critical given the simple question-and-answer nature of the tool.

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, so the schema contains no parameter information. The description does not discuss parameters (none exist), but per the rubric, a 0-parameter tool gets a baseline of 4. The description adds no parameter details because there are none to add.

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 uses a specific verb ('Counts') and clearly identifies the resource ('per layer', 'inbox'). It explains what is counted (decisions landed today) and the time window ('local calendar day'), which distinguishes it from sibling tools that likely provide individual records or different views.

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 gives clear context for when to use this tool: to obtain daily lifecycle counts and pending inbox volume. It does not explicitly state alternatives or exclusions, but the scoped purpose ('per layer', 'today') implies the intended usage without ambiguity.

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