interpolate_1d

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

No annotations are provided, so the description carries the full burden. It discloses only that scipy interp1d is used and that linear/quadratic/cubic modes exist, but says nothing about extrapolation behavior, ordering or monotonicity requirements for x, edge handling, or the return shape.

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?

A single front-loaded sentence with no filler; the verb and target are stated first and the implementation detail trails. It is efficient, though it errs toward brevity at the cost of 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 4-parameter numerical tool with no annotations, no output schema, and 0% schema coverage, the description is too thin. Missing are the shape/format expectations for x, y, xi (array vs path-like string) and the return value nature.

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 must compensate, and it largely does not. It implies y is a function of x and that xi are target points, and it enumerates the kind options, but x, y, and xi are only titled 'X'/'Y'/'Xi' in the schema with no documented array-vs-string semantics.

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 (interpolate) and resource (y(x) at points xi) and names the underlying implementation (scipy interp1d), which is enough to differentiate it from numeric siblings like curve_fit or linear_regression. It does not explicitly contrast with any sibling tool, so it stops short of a 5.

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?

There is no statement of when to use this tool versus alternatives such as curve_fit or a regression tool, nor any exclusions or prerequisites. The agent must infer the use case from the verb alone.

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