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

With no annotations, the description carries the full behavioral burden. It discloses the computed outputs, which is useful, but omits behavioral details such as handling of mismatched array lengths, missing values, or whether the intercept is included. It implies a pure computation with no side effects but does not state it explicitly.

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 compact sentence with no filler, front-loaded with the operation and followed by a list of outputs. Every word 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?

For a simple two-parameter regression tool without output schema or annotations, the description covers the core output contract. However, it leaves input constraints (equal-length arrays) and error behavior implicit, and does not cross-reference sibling tools. It is adequate but has clear gaps.

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 coverage is 0%, so the description must add meaning to the bare array parameters. It does clarify roles: 'of y on x' establishes y as dependent and x as independent. However, it does not specify that the arrays must be numeric, equal in length, or how empty arrays are handled.

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 statistical operation ('simple linear regression of y on x') and enumerates the outputs (slope, intercept, r, r², slope std error, p-value, equation). This clearly differentiates it from sibling tools like hypothesis_test or descriptive_stats.

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 intended use is implied by the phrase 'simple linear regression' — an agent can infer it is for bivariate linear relationships. However, it does not explicitly say when to prefer this over siblings, nor does it mention exclusions like multiple regression or prerequisites such as equal-length arrays.

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