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

Annotations already provide readOnly, idempotent, and non-destructive hints, and the description adds valuable behavioral caveats: 'a target is never an actual, a disclosed holding is never an approval, commitment dollars repeat across reports... nothing predictive is published.' This materially informs the agent about data reliability and interpretation, going well 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 dense but not bloated; every sentence contributes, with the core dataset definition front-loaded and entity_type behavior following naturally. It is long for a tool description, but the complexity of the tool warrants it, and the later caveat sentence earns its place by conveying important data-provenance warnings.

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 tool with 14 parameters and no output schema, the description is notably complete: it names the result shape (compact cards with dfx:al: ids, assets, funded status, targets, commitment counts), explains default query behavior, and warns about data semantics. It does not explain sort options, pagination, or filter behavior beyond entity_type, but those are partially self-evident from the schema and sibling patterns.

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 29% schema description coverage ablutions, the description compensates for the most important parameter, entity_type, by explaining the semantic difference between consultant, manager, and fund modes, and for query via alias-aware search. However, it leaves many other parameters (sort, state, allocator_class, with_policy, min_assets_usd, etc.) without added meaning, so the description only partially covers the parameter space.

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 identifies the tool as a search across the 'capital-owner graph' and explicitly enumerates the entity types it covers: allocators, consultants, managers, and funds. It goes beyond the tool name by specifying data sources (Census, Form 5500, IRS) and the meaning of each entity_type mode, making it immediately distinguishable from siblings like get_allocator or search_entities.

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 provides clear internal routing guidance: it defines what happens with a bare query versus each entity_type value, so an agent knows which mode to choose. It does not explicitly contrast this tool with sibling search/get tools, but the entity_type distinctions supply strong contextual direction.

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