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

The annotations already declare this a safe read-only, idempotent operation. The description adds meaningful caveats beyond the annotations: that estimates are labelled, nothing predictive is published, and reported figures must not be conflated with approvals or approvals. This warns an agent against misinterpreting overlapping dollar figures or targets, which is valuable behavioral and output-interpretation context.

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 long but dense: the main content is front-loaded and organized by entity type, and every clause adds a specific data category or caveat. It is structured enough for the tool's breadth, though the first sentence is heavy.

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?

There is no output schema, so the description carries the full burden of explaining return values. It covers the return categories for allocators, consultants, managers, and funds, names get_entity for same_as links, and adds data-quality caveats about estimates and estimates. The description is complete enough for an agent to understand what the call returns and how to interpret its contents.

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 only parameter, dfx_id, has a complete schema description including the ID format and search sources. The tool description adds little beyond the schema's own parameter documentation, so with 100% coverage the baseline of 3 is appropriate.

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 defines the tool as returning a full profile for an allocator, consultant, manager, or fund and enumerates the major data categories returned for each. It is specific and unambiguous, and it names get_entity for same_as links, though it does not explicitly differentiate get_allocator from related sibling getters like get_entity or get_pe_firm.

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 useful context about what data is included for each entity type, so an agent can infer when a full-entity lookup is appropriate. It does not state explicit when to use this tool versus alternatives or which sibling to choose for narrower lookups, leaving the choice mostly implicit.

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