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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states that the server returns proportions, percentage-point difference, and interval, but does NOT determine significance – the user must. It also discloses that houses are never computed and warns about corpus bias toward public biographies. This is thorough and prevents misinterpretation.

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 a single paragraph with a clear logical flow: purpose, example, then three warnings/constraints. Each sentence adds essential information, and the core purpose is front-loaded. It is slightly dense but not verbose; every sentence earns its place.

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?

Given the tool's complexity (statistical comparison), absence of annotations, and presence of an output schema (which likely describes return format), the description covers the critical aspects: what it does, what it doesn't do, how to interpret results (significance is user's decision), and data limitations. It does not describe pagination or exact response structure, but the output schema presumably handles that, so the description is sufficiently complete for an agent to decide and invoke correctly.

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 description explains the semantic role of the key parameters: graha and znak together define the feature, occupation_qid defines the group. It also clarifies that lang has a default. Since schema description coverage is only 40% (only occupation and occupation_qid have descriptions), the description compensates by explaining how the core parameters interact, though it does not detail enum meanings (e.g., zodiac sign ranges) – but those are in the schema enums, so the added value is meaningful but not exhaustive.

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 states the tool's purpose: comparing the proportion of a feature (graha+sign) in a defined group (occupation_qid) against the whole corpus, with a concrete example ('Jupiter in Sagittarius among entrepreneurs'). It is specific about the verb (compare), the resource (group vs corpus), and the scope, and distinguishes itself from generic search tools like corpus_search by focusing on statistical comparison.

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 usage context: it is used when you want to compare a feature's prevalence in a group vs the corpus. It explicitly states what it does NOT do (houses are never calculated, because birth time is mostly absent), which helps avoid misuse. However, it does not explicitly name alternative sibling tools or provide 'when not to use' conditions, so it falls slightly short of a 5.

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