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

Annotations already cover the read-only, idempotent, closed-world safety profile; the description adds substantial context beyond that: the data provenance (Monte-Carlo sample of 5,000 Indian birth charts through AstroCalc's yoga engine), the exact return shape ('1 in N', percentage, rarity label, one-line meaning, Learn link), and the aggregation rule that a bare 'Kaal Sarp' counts all 12 types.

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?

Purpose is front-loaded in the first clause, followed by examples, user phrasings, input tolerance, and return format in a logical order. It is dense but nearly every sentence carries information; the multi-query examples are slightly verbose but genuinely aid intent matching.

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 no output schema, the description fully carries the return-value burden and does so well, plus it documents the data source and language tolerance. The only shortfall is not clarifying single-vs-batch parameter behavior, which matters for a 3-parameter tool with both 'yoga' and 'yogas'.

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%, so the baseline is 3, but the description still adds real meaning: it accepts English, Hindi or Hinglish names ('yog' works too), which the schema does not say. It does not explicitly explain the relationship between the singular 'yoga' and the batch 'yogas' array, leaving one small gap.

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 precise verb+resource: reports how common or rare a specific yoga (chart combination) is, with named examples (Gaja Kesari, Kaal Sarp, etc.). This is clearly distinct from sibling tools like full_chart or kundli_milan, which compute charts rather than look up statistical rarity.

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 a concrete usage trigger: 'when someone has been told their chart has a special, lucky or worrying yoga and wants perspective,' backed by realistic example queries. It also clarifies 'no birth data needed,' implicitly steering the agent away from chart-computation siblings, though it never names an explicit alternative or when-not-to-use case.

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