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

With no annotations, the description carries the full burden and does disclose meaningful behavior: some plots auto-fit a model from design_data+response_col, and overlaid_contour fits one RSM per response and returns a combined in-spec mask while fixed_factors pins the other factors. It does not cover safety/permission profile or failure behavior, but the computational semantics are well surfaced.

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?

Front-loads the purpose then organizes content by plot category (analysis, model, optimization, overlaid_contour), which is efficient given 14 plot types. It is dense with parentheticals but nearly every clause carries a distinct instruction, with minimal filler.

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?

An output schema exists, so return values need not be explained, and the description focuses appropriately on the plot-type/data-requirement matrix that determines a correct call. It is largely complete for the core task, though the unaddressed optional parameters and the absence of any auth/api_key note leave minor gaps for a 17-parameter tool.

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 compensate and largely does: it gives meaning for alpha (reference threshold for normal_effects), response_specs (list of {name, lower/upper}), factor_x/factor_y, fixed_factors (pins other factors), and the design/model/analysis input params. Several params (grid_size, components, factors, response_x, response_y, api_key, optimization_result) remain opaque, so coverage is partial rather than complete.

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 and resource ('Generate plot data for DoE visualizations') and immediately scopes it to 14 enumerated plot types, making it trivially distinguishable from sibling DoE tools like covadoe_fit_rsm or covadoe_analyze that are not plotting tools.

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?

Groups the 14 plot types and states the input each category requires (analysis plots need analysis_result or design_data+response_col to auto-fit; model plots need model_result; optimization plots), which effectively tells the agent how to invoke per plot type. It stops short of explicitly naming an alternative tool to use instead, but provides clear invocation context.

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