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

With no annotations, the description carries the burden. It discloses useful behavior: returned data are aggregate counts broken down by time/type/region with fixed lookback windows (6y, 10y, 3y). It does not state the return format or clarify 'report and live snapshot stats,' and read-only/side-effect behavior is only implied by 'get' and 'aggregates.'

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?

A single dense sentence front-loads the core concept and packs in data sources, grouping, and time ranges without redundancy. The trailing 'report and live snapshot stats' is vague and slightly weakens the otherwise tight structure.

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?

The description covers data sources and time ranges, which is helpful for a parameterless tool. However, with no output schema, it does not explain the result shape, and the meaning of 'report and live snapshot stats' is unclear, leaving an agent without a full picture of what will be returned.

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 is empty and there are no parameters to document, so the baseline of 4 applies. The description adds no parameter detail because none 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 identifies the tool's resource ('historical aggregates') and enumerates specific datasets and aggregation dimensions (VPD crime by year/month/type, USGS quakes per region, GDACS events) with time ranges. It is clear but does not explicitly differentiate itself from siblings like get_crime_year or get_incidents.

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 choose this tool over get_crime_year, get_incidents, or other siblings. The word 'Historical' implies a trend-use case, but there are no explicit conditions, exclusions, or alternative routing.

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