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Scientific Calculator

by ai.saifs
OverviewDiscussionsAdmin

Probability Calculator

calculator_probability
Read-only

P = favorable / total. Returns decimal and percentage.

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": {
      "favorable": "<favorable>",
      "total": "<total>"
    },
    "name": "calculator_probability"
  }
}

Input Schema

NameRequiredDescriptionDefault
totalYesTotal outcomes (>0).
favorableYesFavorable outcomes (>=0).

Output Schema

NameRequiredDescriptionDefault
expressionYes
percentageNo
probabilityYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare it a safe, closed-world read-only operation, so the safety burden is lifted. The description adds the formula and states the return shape (decimal and percentage), but that return detail is largely duplicated by the existing output schema, and edge-case behavior is unstated.

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

Conciseness4/5

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

Two short sentences with the formula front-loaded and zero preamble. The second sentence is partly redundant with the output schema, which keeps it from a perfect score.

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?

For a two-parameter pure-computation tool with full schema coverage, rich annotations, and an output schema, the description covers the essentials. Only unstated edge-case semantics (e.g., favorable > total, favorable = 0) remain as a minor 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 coverage is 100% with per-parameter descriptions and examples, so the baseline is 3. The formula does clarify which argument is numerator versus denominator, a small genuine addition, but no format or constraint guidance beyond the schema is offered.

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?

The description states the exact formula ('P = favorable / total'), which pins down a specific computation rather than just restating the name. It is clear on its own, but never distinguishes itself from adjacent siblings such as calculator_ratio, calculator_proportion, or calculator_random_distribution.

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

Usage Guidelines2/5

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

There is no guidance on when to pick this over alternatives, no preconditions (e.g., favorable <= total), and no mention of related tools. The agent must infer usage purely from the formula.

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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