plot_scatter

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

The description discloses two useful behavioral traits: auto-sampling beyond the `sample` parameter and the output format (JSON + inline PNG). Since there are no annotations, this is valuable. However, it does not mention what happens with missing values, non-numeric columns, or whether the tool has any side effects, which would be relevant for a plotting operation.

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 extremely concise, using only three short fragments. It front-loads the core purpose and packs sampling and output format into two additional clauses without wasted words. Every sentence 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?

Given the tool has 5 parameters, no output schema, and no annotations, the description is incomplete. It does not specify what the JSON part of the output contains, how to interpret the PNG, or the role of `color` and `source_id`. The lack of usage guidance also makes it hard to decide when to invoke this tool over siblings, especially with many other plotting tools present.

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 0%, so the description must compensate. It only implicitly covers x and y, and mentions `sample` in passing ('Auto-samples >`sample` rows'), but does not explain the exact meaning of `sample` (whether it's a target number or threshold). The `color` and `source_id` parameters are entirely unaddressed, leaving significant gaps for the agent.

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 identifies the tool as creating a scatter plot of x vs y, which is specific and distinguishes it from other plot types like distribution, boxplot, or heatmap. However, it uses a noun phrase rather than an explicit verb like 'plot' or 'visualize', so it could be clearer, but the meaning is unambiguous.

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?

No explicit guidance is given about when to use this tool versus alternatives such as plot_correlation_heatmap or plot_timeseries. The purpose implies usage for two-variable relationships, but there are no exclusions, prerequisites, or alternative recommendations, leaving the agent to infer the appropriate context.

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