set_measurement

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

No annotations are provided, so the description carries the full burden of behavioral disclosure for what is a mutation tool. It does not say whether the change is persisted to the file, what happens without the optional save path, whether it requires an attached/running instance, or whether the change is reversible. Only the conceptual meaning of the setting is conveyed.

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?

Two tight sentences, purpose front-loaded, no filler. Every clause adds information about what the tool does and why it matters.

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 and no annotations, the description should carry more: for a 6-parameter mutation tool it omits persistence, attach/live-edit semantics, and failure modes. Purpose is complete, but operational context is not.

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 83%, so the baseline is 3. The description adds meaning beyond the schema by listing the measurement levels and explaining their analytical effect (factor vs covariate), clarifying what the 'type' parameter actually controls. It still omits any note on variable indexing or save behavior.

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+resource ('Set the measurement level of a variable') and enumerates the accepted levels. The second sentence explains the downstream consequence (factor vs covariate), which distinguishes it cleanly from siblings like rename_variables or write_variables.

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 explains WHY the setting matters (STATISTICA treats the variable as factor or covariate), which implies usage, but it never states when to call this tool versus alternatives or any prerequisites (e.g. file must be loaded, analysis must be configured). Usage is inferable but not stated.

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