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Math MCP Learning

by io.github.clouatre-labs

Function Plotter

plot_function
Read-onlyIdempotent

Generate mathematical function plots (requires matplotlib).

Examples: plot_function("x**2", (-5, 5)) plot_function("sin(x)", (-3.14, 3.14))

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
x_rangeYesX-axis range as (min, max), e.g., (-5.0, 5.0)
expressionYesMathematical expression to plot, e.g., "x**2" or "sin(x)". Must be <= MAX_EXPRESSION_LENGTH characters. Example: "x**2"
num_pointsNoNumber of sample points to plot along x_range, e.g., 100

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds the 'requires matplotlib' dependency but does not disclose return format, whether the plot is displayed/saved, or error behavior. With annotations covering side-effect safety, this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is two sentences plus two examples. The purpose is front-loaded, and every element earns its place. No redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a relatively simple tool with full schema coverage and safety annotations, the description is mostly sufficient. However, there is no output schema, and the description does not explain what the tool returns (e.g., plot object, file path), nor does it mention potential pitfalls. This creates a gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for all parameters. The description adds example usage but no additional semantic information beyond the schema, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states 'Generate mathematical function plots' with concrete examples like "x**2" and "sin(x)", clearly distinguishing it from sibling chart tools that plot data points rather than math expressions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose implies this is for mathematical expressions, but no explicit alternatives or exclusions are given. Examples show how to call it, but there is no guidance on when to choose this over plot_line_chart or plot_scatter.

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

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TDQS

A4/5.0
Disambiguation4/5

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.

Naming Consistency5/5

Tool names follow a clear, consistent prefix pattern: calc_*, matrix_*, plot_*, and workspace_*. This makes it easy to infer related functionality at a glance.

Tool Count4/5

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.

Completeness4/5

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.

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