plot_pareto_front

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

No annotations exist, so the description carries the full burden. It explains the return type (image) and parameter conditions, but does not explicitly state that the tool is read-only, has no side effects, or describe the image format. This leaves some ambiguity about behavior.

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 structured as a docstring with parameter explanations. It front-loads the purpose and uses bullet-style args. Some explanations (e.g., targets) are slightly verbose but each sentence adds value. Could be more concise but remains effective.

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?

Given the complexity of multi-objective optimization and lack of output schema, the description covers parameter semantics and conditions well. It does not specify the image output format or prerequisites (e.g., at least 2 objectives), but the parameter constraints are clear enough for an intermediate user.

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?

With 0% schema description coverage, the description compensates fully by detailing each parameter: target_names (axis titles, default behavior), include_dominated_trials (flag function), and targets (index usage and constraints). It adds significant meaning beyond the schema types and defaults.

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 states the tool returns a Pareto front plot as an image for multi-objective optimization. It uses specific verbs ('Return') and resources ('Pareto front plot'), and the context of sibling plot tools (contour, slice, etc.) helps differentiate it.

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 necessary conditions for parameters (e.g., target_names must be specified if no completed trials, targets must be specified if objectives not 2 or 3). It provides implicit guidance for multi-objective use, but does not explicitly say when NOT to use it versus other plot tools.

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