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

Annotations already establish read-only, idempotent, non-destructive behavior, so the description's job is to add context. It does: only SEC-registered advisers are included, every metric is derived from filed fields, formulas live in get_ria_practice, and book size/revenue are explicitly unavailable. It does not describe pagination or response shape, but that is partially covered by the cursor and limit schema fields.

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 operation and resource, then packs the filter dimensions into one dense sentence and closes with a valuable limitation. The complete archetype enum is redundant with the schema and adds token cost, but it is the only real inefficiency; otherwise the text is compact and purposeful.

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 20-parameter screening tool with no output schema, this is nearly complete: it names the target population, all screenable dimensions, sort options, data provenance, formula source, and two hard exclusions. It still relies on the schema for pagination and does not spell out units for share-type thresholds. Overall an agent has enough context to formulate a correct query.

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 only 10% schema description coverage, the description carries most of the semantic load by mapping each major filter group to a named metric or flag: RAUM band, growth, RAUM per advisor, private-client/HNW shares, private funds, custody, wrap, pension consulting, and financial planning. It also lists all sort keys and directs agents to get_ria_practice for exact metric formulas. It leaves units for share-based thresholds implicit, but the overall parameter meaning is far clearer than the bare schema.

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 names a specific operation ('screened'), a specific resource ('SEC-registered advisers on the practice layer'), and enumerates the screening dimensions: archetypes, class, RAUM bands, growth, flags, and sort orders. It also draws a boundary with 'no book size, no revenue,' which helps distinguish it from other RIA search tools. This is a clear, specific statement of what the tool does.

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 intended use case is implied—screen advisers by practice-layer characteristics—and the description explicitly excludes book size and revenue. However, it never names a sibling alternative or states when to prefer search_ria_practices over search_ria or get_ria_practice. The only cross-reference, 'formula in get_ria_practice,' points to where formulas live rather than when to invoke this tool. Guidance is therefore mostly implicit.

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