probability.chi_square_ppf

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

Annotations already establish readOnly/idempotent/closed-world, and the description adds real behavioral detail beyond them: the underlying scipy float64 computation and the 10-significant-digit rounding of the returned string. It omits what happens with out-of-domain input (rejection vs. error), which is the main remaining gap.

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 sentences, no filler, and the core purpose comes first followed by parameter constraints and then the practical α hint. Every sentence carries 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?

For a two-parameter numeric routine with an output schema already documenting return shape, the description covers purpose, both parameter domains, numeric precision, and the canonical statistical use case. Nothing an agent needs in order to call it correctly is missing.

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%, so the description carries the full burden and does so: q is constrained to (0,1), df is a positive decimal, both are strings, and the α-to-1-α mapping clarifies the intent of q. The 'decimal string' format expectation for df is stated, though precision/format rules for q are only implied.

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 states a specific verb and resource ('computes the quantile of the chi-square distribution'), which is self-differentiating from the sibling cdf/pdf variants and from other distributions' ppf tools. An agent can identify the operation without opening the schema.

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?

It gives concrete usage guidance by explaining that the critical value for significance level α is obtained by passing 1-α as q, which is the main point of confusion for ppf callers. It does not, however, state when this tool should be chosen over the cdf sibling or the related f_ppf/normal_ppf tools.

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