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apetta

Vibe Math MCP

by apetta

Derivative Calculator

derivative
Read-onlyIdempotent

Compute symbolic and numerical derivatives of mathematical expressions, including higher-order and partial derivatives, with optional evaluation at a given point.

Instructions

Compute symbolic and numerical derivatives with support for higher orders and partial derivatives.

Examples:

FIRST DERIVATIVE: expression="x^3 + 2x^2", variable="x", order=1 Result: derivative="3x^2 + 4*x"

SECOND DERIVATIVE (acceleration/concavity): expression="x^3", variable="x", order=2 Result: derivative="6*x"

EVALUATE AT POINT: expression="sin(x)", variable="x", order=1, point=0 Result: derivative="cos(x)", value_at_point=1.0

PRODUCT RULE: expression="sin(x)*cos(x)", variable="x", order=1 Result: derivative="cos(x)^2 - sin(x)^2"

PARTIAL DERIVATIVE: expression="x^2*y", variable="y", order=1 Result: derivative="x^2" (treating x as constant)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoOptional annotation to label this calculation (e.g., 'Bond A PV', 'Q2 revenue'). Appears in results for easy identification.
output_modeNoOutput format: full (default), compact, minimal, value, or final. See batch_execute tool for details.full
expressionYesMathematical expression to differentiate (e.g., 'x^3 + 2*x^2', 'sin(x)')
variableYesVariable to differentiate with respect to (e.g., 'x', 't')
orderNoDerivative order (1=first derivative, 2=second, etc.)
pointNoOptional point for numerical evaluation of the derivative

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

The description adds behavioral context beyond annotations: it specifies that results include derivative expression and optionally value_at_point. Annotations already declare readOnlyHint and idempotentHint, so the description complements these without contradiction.

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 concise: a single summary sentence followed by clear, well-structured examples. No unnecessary text, and examples are front-loaded to illustrate common patterns efficiently.

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

Completeness5/5

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

The description covers all major use cases (symbolic, numerical, higher order, partial derivatives, evaluation at point) and includes enough variety to fully guide an agent. The tool has an output schema, so explaining return format is unnecessary.

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%, so all parameters are well-documented. The description reinforces parameter meaning through examples but does not add substantial new semantic information beyond what the schema provides. 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?

The description starts with a clear verb and resource: 'Compute symbolic and numerical derivatives with support for higher orders and partial derivatives.' This is specific and distinguishes the tool from siblings like integral or calculate.

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

Usage Guidelines4/5

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

The description provides multiple examples showing different use cases (first, second, evaluation, product rule, partial derivative), which implicitly guides when to use specific parameter combinations. However, it does not explicitly state when not to use this tool or direct users to alternatives, though the sibling names and context signals make differentiation apparent.

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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