plot_functionGenerate mathematical function plots (requires matplotlib).
| Name | Required | Description | Default |
|---|---|---|---|
| x_range | Yes | X-axis range as (min, max), e.g., (-5.0, 5.0) | |
| expression | Yes | Mathematical expression to plot, e.g., "x**2" or "sin(x)". Must be <= MAX_EXPRESSION_LENGTH characters. Example: "x**2" | |
| num_points | No | Number of sample points to plot along x_range, e.g., 100 |
Changes observed during successful MCP inspections.
Input schema / properties / x_range / descriptionAdded value: +"X-axis range as (min, max), e.g., (-5.0, 5.0)"Input schema / properties / expression / descriptionAdded value: +"Mathematical expression to plot, e.g., \"x**2\" or \"sin(x)\". Must be <= MAX_EXPRESSION_LENGTH characters. Example: \"x**2\""Input schema / properties / num_points / descriptionAdded value: +"Number of sample points to plot along x_range, e.g., 100"Output schema / (root)Previous value: -{
- "additionalProperties": true,
- "type": "object"
-}New value: +nullInput schema / additionalPropertiesAdded value: +falseInput schema / properties / expression / titleRemoved value: -"Expression"Input schema / properties / num_points / titleRemoved value: -"Num Points"Input schema / properties / x_range / titleRemoved value: -"X Range"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds one genuinely useful fact beyond the annotations — the matplotlib dependency — but says nothing about what is returned (image, file, data) or how invalid expressions are handled.
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?
A single front-loaded sentence with no filler or repetition. It is efficient, though its brevity shades into under-specification rather than optimal conciseness.
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?
No output schema exists, so the description carries the burden of explaining what the tool returns — and it does not say whether the result is an image, a file path, or inline data. Combined with the absence of usage guidance, the definition is not complete enough for an agent to know what to expect from a successful call.
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%, so expression, x_range, and num_points are fully documented in the schema with examples and bounds. The description adds no additional parameter meaning, which is the expected baseline when the schema does the heavy lifting.
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?
States a specific verb (Generate) and resource (mathematical function plots), which implicitly separates it from the histogram/scatter/box-plot siblings. However, it never names those siblings or contrasts itself with them, so an agent must infer the distinction from the wording alone.
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?
There is no when-to-use guidance, no exclusions, and no mention of alternatives such as plot_line_chart or calc_expression. The agent gets no help deciding between this tool and the six other plotting/calculation siblings.
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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