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

Annotations already establish read-only, idempotent, non-destructive behavior; the description adds genuinely non-obvious caveats: counts are registration-date based, bulk re-registrations are excluded, RAUM sums double count affiliates, and the data is explicitly not predictive. This goes well beyond what annotations alone convey.

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 key concept and then packed with useful detail; every clause contributes meaning. It is dense and could be slightly easier to scan if broken into bullets, but it earns its length given the three-view complexity.

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 complex 8-parameter tool with no output schema and no required parameters, the description covers view definitions, ranking semantics, filters, defaults, and data-quality caveats. It does not describe the response envelope or pagination/limit behavior, but an agent has enough to safely invoke and interpret a first call.

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?

With schema coverage at only 13%, the description rightly compensates by explaining what sort values and filters do in each view, and reveals the $100M RAUM default exclusion relevant to min_raum_usd. It does not explicitly map limit, min_advisors, or wealth_only, but the view text and self-descriptive enum values make most parameters inferable.

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 states a specific aggregate-statistics resource and enumerates three concrete views (state_stats, flows, growth) with the metrics each contains. This clearly distinguishes the tool from entity-level get_ria_* and search_ria_* siblings without opening any schemas.

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 strong context for selecting among the three views and notes important exclusions and defaults, which implies when each view is appropriate. However, it never explicitly contrasts this tool with siblings like search_ria_changes or get_ria_firm, so when-to-use-vs-alternatives is left to inference.

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