linear_fit

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

With no annotations, the description carries the full burden of disclosing side effects. It only states that the reference is not modified, but it never states that the target view is modified in place, what happens to rejection thresholds, or how failures are reported. This is a meaningful transparency gap for a process that likely mutates the input view.

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 two sentences with no wasted words. The primary action is stated first, and the important caveat about the reference view is included succinctly.

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?

This is a mutating process with no annotations and no output schema, so the description must do more. It omits whether the target view is changed in place, what the result of the operation is, and any guidance on how reject_low/reject_high affect the fit. The description is too sparse to fully support correct invocation.

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 schema already documents all four parameters and their defaults. The description adds only the conceptual purpose of view_id and reference_id ('match a reference view') but no additional format, interaction, or edge-case details, so the baseline 3 is appropriate.

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 names a specific process (LinearFit), states its action ('scale a linear image'), and identifies the target/reference relationship. It also distinguishes an important boundary ('The reference is not modified'). It does not explicitly differentiate from the similarly named sibling align_to_reference, but the 'scale' wording implies intensity matching.

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 a clear context for when to use the tool: when a linear image needs intensity scaling to match a reference view. However, it gives no when-not-to-use guidance and does not explicitly route away from sibling tools such as align_to_reference, leaving the comparison to inference.

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