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Math MCP Learning

by io.github.clouatre-labs

Line Chart

plot_line_chart
Read-onlyIdempotent

Create a line chart from data points (requires matplotlib).

Note: Use for general XY data. For time-series price data with optional moving average, use plot_financial_line instead.

Examples: plot_line_chart([1, 2, 3, 4], [1, 4, 9, 16], title="Squares") plot_line_chart([0, 1, 2], [0, 1, 4], color='red', x_label='Time', y_label='Distance')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colorNoLine color (name or hex code, e.g., 'blue', '#2E86AB')
titleNoChart title string, e.g., 'Squares'Line Chart
x_dataYesX-axis data points, e.g., [1, 2, 3, 4]
y_dataYesY-axis data points, e.g., [1, 4, 9, 16]
x_labelNoX-axis label, e.g., 'Time'X
y_labelNoY-axis label, e.g., 'Distance'Y
show_gridNoWhether to display grid lines

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint. The description adds the prerequisite 'requires matplotlib' and scopes the tool to general XY data. It does not describe return values or rendering side effects, but the key behavioral traits are covered by annotations and the tool's purpose.

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?

The description is brief but information-dense: a one-sentence purpose, a usage note with a sibling alternative, and two illustrative examples. No filler or redundant content.

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

Completeness4/5

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

Given the schema covers parameters and annotations cover safety, the description adds the required matplotlib dependency and the usage distinction from plot_financial_line. It does not explain what the tool returns, but for a plotting tool this may be self-evident. Overall it is sufficiently complete for agent use.

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

Parameters4/5

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

Schema describes all 7 parameters with 100% coverage. The description's examples supplement the schema by demonstrating call syntax and parameter order (e.g., x_data, y_data first), which adds practical usage meaning beyond the schema's static property list.

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?

The description states 'Create a line chart from data points' with a specific verb and resource. It also explicitly differentiates from plot_financial_line, making the tool's scope clear and distinct among siblings.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'Use for general XY data. For time-series price data with optional moving average, use plot_financial_line instead.' This tells the agent when to use this tool and points to an alternative.

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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