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

Annotations already cover read-only, idempotent, non-destructive safety, so the description earns credit for going further: pagination, the default sort ordering, how views and revenue sorts are derived (sampled-video views, reported three-month USD gross), and the important 'missing metrics are not zero' data-quality warning. It also discloses the return envelope (provenance, directory_scope, unknowns) and a caveat that candidate inclusion does not confirm AI production. Only rate limits and the meaning of 'status' are left undisclosed.

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?

Three tight sentences, front-loaded with the verb and resource, then ordering behavior, then data-quality caveats. Every clause carries information; nothing is redundant with the schema or annotations.

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?

With an output schema present, return-value explanation is optional, and the description nonetheless signals the key caveats (missing metrics, unknowns, candidate ambiguity). For an 8-parameter tool with zero schema descriptions, the only thing an agent still lacks is the semantics of the status filter and limit/offset behavior.

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?

Schema description coverage is 0% across 8 parameters, so the description carries the burden and largely does: it explains what query matches (name/handle, provenance), the audience/views/revenue sort semantics and default, and that language, platform and region are filters. It does not explain the status enum (confirmed/reported/unverified/unknown) or limit/offset bounds, so a real gap remains.

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?

States a specific verb (search) and resource (monk profiles and pending research candidates) with the filterable dimensions enumerated. It is clearly a list/search tool versus the single-record sibling get_ai_monk, though it never names a sibling explicitly. The exclusion note ('Excludes illustrated/watercolor storytelling channels') sharpens scope further.

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?

Usage is implied through the filter list and the caveat about candidate inclusion, so an agent can infer this is the discovery entry point before calling get_ai_monk. However there is no explicit 'use this when / use X instead' routing against the six sibling tools. The exclusion clause is the only hard boundary stated.

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