plot_histogram

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

With no annotations, the description carries the transparency burden. It does well by disclosing the binning formula, the statistics drawn on the plot, the supported inline_data shapes, and a pointer to SPEC.md for limits and type domains. It does not describe what the tool returns or how the plot is delivered, which is a minor gap.

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 core purpose is front-loaded, followed by the binning rule and input format details. Some clutter exists ('工具 22，核心实现'), but overall the description is compact and scannable.

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?

The description covers core plotting behavior and inline data constraints, but it lacks return/output semantics and does not adequately explain file_path or column behavior. Since there is no output schema, some of this burden falls on the description, and it is only partially met.

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. It adds meaning for inline_data and file_path by stating they are mutually exclusive and by describing inline_data formats. However, 'column' is only implied by 'single-column' and its null/default behavior is not explained, leaving an important parameter underdocumented.

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 this is a single-column distribution histogram and specifies what the plot displays (n/mean/std). Its identity as a histogram distinguishes it from siblings like plot_scatter, plot_heatmap, and plot_box, even without naming them explicitly.

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?

Usage is implied by '单列分布直方图' and the inline_data/file_path mutual-exclusivity note. However, there is no explicit guidance about when to choose this tool over plot_box, plot_scatter, or other visualizations, and no alternative tool is mentioned.

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