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

Annotations already declare read-only, open-world, idempotent, and non-destructive. The description adds context about the nature of the data: it returns changed metrics with old/new values and reasons, which is useful behavioral insight. It does not contradict 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?

Two sentences with no waste. The core purpose is front-loaded, and the use case adds value without bloat.

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 one-parameter read tool with annotations covering safety, the description gives a high-level idea of the return content (old/new values and reasons) and a use case. It does not specify response structure, but without an output schema that's acceptable. It is reasonably complete.

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?

Schema coverage is 0%, so the description must compensate. It only refers to 'this asset' which implies symbol is the asset identifier, but does not explain the expected format (e.g., ticker vs. ID) or provide any details about the parameter. For a single parameter, this is minimal compensation.

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 metrics that changed after first publication, with old/new values and reasons. It distinguishes itself from siblings like get_asset (current values) and get_metric_revisions (metric-centric revisions) by focusing on asset-level revisions.

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 a clear use case ('an agent that quoted an earlier number can find out here that it moved') but does not explicitly name alternatives or when not to use. It implies usage when you need to detect changes in an asset's metrics, but lacks exclusions.

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