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

Annotations already establish read-only, idempotent, and non-destructive behavior, so the bar is lower. The description adds meaningful behavioral context: ranking by verification date and that the tool ignores the user's topic entirely, which goes beyond the structured annotations.

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 two sentences with no filler. The core behavior and freshness signal are front-loaded, followed by explicit usage guidance and exclusion of irrelevant alternatives. Every 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?

With only one optional parameter, an output schema present, and annotations covering safety, the description supplies the missing contextual pieces: when to use the tool, what it ignores, and how it differs from siblings. Nothing critical is missing for an agent to invoke it 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 documents the only parameter (limit) with a clear description, default, min, and max, so schema description coverage is 100%. The tool description adds no further parameter details, which is acceptable per the baseline; the schema carries the semantic weight.

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 the tool returns 'Most recently verified knowledge objects' and 'ranks by verification date,' giving a specific verb and resource. It also distinguishes itself from siblings by explicitly noting it ignores the user's topic, which sets it apart from search and get_topic.

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?

It provides explicit usage context: use this to judge how current the corpus is or to see what changed since last read. It also names alternatives and the condition that selects them: 'use search or get_topic when you want objects that are relevant rather than recent.' This is model guidance.

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