plot_histogramCreate statistical histograms (requires matplotlib).
Examples: plot_histogram([1.0, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0]) plot_histogram([10, 20, 30, 40, 50], bins=5, title="Test Scores")
| Name | Required | Description | Default |
|---|---|---|---|
| bins | No | Number of histogram bins, e.g., 20 | |
| data | Yes | List of numeric values to bin, e.g., [1.0, 2.0, 2.5, 3.0] | |
| title | No | Chart title string, e.g., 'Data Distribution' | Data Distribution |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint, covering safety and side-effect concerns. The description adds the external dependency on matplotlib, which is useful behavioral context. However, it does not clarify whether the tool displays the plot, saves it, or returns a figure object, leaving some behavioral ambiguity beyond the annotations.
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 brief and front-loaded with the core action, followed by two practical examples. Every sentence earns its place; there is no wasted wording, and the examples improve clarity without bloat.
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 tool is simple (3 parameters, all documented, no output schema), and the description covers the essential usage with examples and a dependency note. It lacks details on the return value or display behavior, but given the low complexity and presence of annotations, the description is nearly complete for invocation purposes.
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 100% with clear descriptions for data, bins, and title. The description's examples (e.g., bins=5, title='Test Scores') illustrate usage but do not add new semantic meaning beyond the schema. Since the schema fully documents each parameter, a baseline 3 is appropriate.
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 immediately states 'Create statistical histograms' with a specific verb and resource, and the tool's name matches. This clearly distinguishes it from sibling plot tools like plot_box_plot and plot_scatter, making its purpose 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?
The description implies usage through the examples, showing how to call the tool with data and optional bins/title. It mentions a prerequisite ('requires matplotlib') but does not explicitly contrast with other plot types or state when to use a histogram over alternatives. Thus usage is implied, not explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinct (calculation, interest, stats, units, matrix ops, plotting, workspace). However, plot_function, plot_line_chart, and plot_financial_line could be confused since they all produce line-like plots, though descriptions note their specific use cases.
Tool names follow a clear, consistent prefix pattern: calc_*, matrix_*, plot_*, and workspace_*. This makes it easy to infer related functionality at a glance.
17 tools is on the higher side but acceptable for the wide math scope (basic arithmetic, statistics, units, matrices, plotting, workspace). Each tool serves a distinct purpose, though a couple like plot_line_chart and plot_function could potentially be consolidated.
Core mathematical operations are well covered: expression evaluation, statistics, unit conversion, matrix operations, and common plot types. Minor gaps exist (e.g., no bar chart, no equation solving), but these are not critical for the server's apparent educational purpose.