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

With no annotations, the description carries the full load and does well: it discloses the 8 local-currency regions vs. USD fallback with `pricingCountry=US` metadata, live-feed vs. static-fallback provenance, and the non-obvious Free-tier credit rule (one-time starting credits that never refill, see aiCreditsNote). It omits anything about response size, ordering, or caching/rate behavior for a list tool that returns a full ladder plus add-ons.

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?

Front-loaded with purpose, then coverage, then return contents, then the sibling exclusion — a sensible ordering. Some density of parentheticals (currency list, credit caveat, plan id) makes it longer than needed, but each clause conveys distinct information.

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?

No output schema exists, and the description compensates well by naming the key returned fields: credit allowance, feature bullets, public signup URL, and provenance. Combined with the plan coverage and fallback rules, an agent can predict the response shape. Slightly short on result volume or ordering.

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 100% with two fully enumerated parameters, so the schema already does the heavy lifting. The description's filter sentence and the fallback explanation largely restate the schema text. Baseline 3 is correct since little is added beyond structured fields.

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 and resource (list HelloBooks pricing plans with monthly + annual prices) and enumerates exactly what is covered: the five-tier ladder, the free `cpa` plan, and two stackable add-ons. It explicitly distinguishes itself from `practice_management_info`, which is the only plausible confusable sibling.

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?

Gives an explicit exclusion in the right place ('HelloCPA Practice Management is a separate product... call practice_management_info, NOT this tool') and describes how to narrow results via `plan` or `country`. It does not distinguish itself from adjacent sibling tools like `list_credit_packs` or `feature_search`, which an agent might otherwise reach for when pricing-adjacent data is wanted.

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