CALCULATE_MCP

calculate_expression

Evaluate math expressions with optional variables. Handles arithmetic, powers, factorial, trigonometry, logs, square roots, min/max, and constants like pi, e, tau.

Instructions

Safely evaluate a mathematical expression with optional numeric variables. Supports arithmetic, powers, factorial, trig, logs, sqrt, min/max, pi, e, and tau.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "expression": "<expression>"
    },
    "name": "calculate_expression"
  }
}

Input Schema

Name	Required	Description	Default
`variables`	No
`expression`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Sep 14, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. 'Safely' is a meaningful signal that evaluation is sandboxed rather than arbitrary code execution, but the description says nothing about error behavior, return type, precision, or limits beyond the schema's 2000-char cap.

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?

Two tight sentences: the core action first, capability scope second. Zero redundancy and nothing buried.

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?

With no output schema and no annotations, the description should say what a call returns (number? string?) and how failures surface. It adequately scopes supported operations but leaves the return contract and error semantics unspecified.

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 0%, so the description must compensate. It clarifies that 'variables' are optional and numeric and are meant to be substituted into the expression, which adds real meaning for the nested object parameter, but it adds nothing about the 'expression' string's syntax, alternates, or length constraint.

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

Purpose4/5

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

States a specific verb and resource ('Safely evaluate a mathematical expression') and enumerates the supported function families, so the agent knows this is a general-purpose math evaluator. It does not name any sibling (e.g. calculate_decimal, solve_root, calculate_matrix) to draw the boundary explicitly, so it stops short of a 5.

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?

Usage is implied by the capability list (trig, logs, sqrt, min/max, constants) rather than stated as when-to-use versus alternatives such as calculate_decimal or solve_root. No exclusions or prerequisites are given, leaving routing to be inferred from the sibling names.

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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