calculator_mcp

percentile

Calculate the pth value in a numeric dataset, revealing the score at or below which a given percentage of data falls.

Instructions

Calculate the pth percentile of a dataset.

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": {
      "numbers": "<numbers>",
      "p": "<p>"
    },
    "name": "percentile"
  }
}

Input Schema

Name	Required	Description	Default
`p`	Yes
`numbers`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 13, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does not disclose the interpolation/quantile convention (e.g., linear interpolation vs nearest-rank), which materially affects the returned value, nor anything about handling of unsorted input, empty arrays, or out-of-range p. For a pure-computation tool this is thin behavioral coverage.

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?

A single, front-loaded sentence with zero wasted words. Structure is optimal for a simple math utility.

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?

An output schema exists, so return-value explanation is unnecessary. However, for a percentile tool the unresolved ambiguity in the p scale and interpolation method is a real completeness gap that the description does not close, leaving an agent to guess semantics that change the result.

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 0% and there are two required parameters, so the description must compensate. It partially does: 'pth percentile' connects to the p parameter and 'dataset' to numbers, but it says nothing about valid p ranges (0-100 vs 0-1), and the schema gives only bare titles like 'P' and 'Numbers'.

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+resource: 'Calculate the pth percentile of a dataset.' An agent knows exactly what operation this performs. It does not, however, differentiate itself from nearby siblings such as quartiles, median, or iqr, which compute related order statistics.

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 when-to-use guidance, no mention of prerequisites, and no reference to alternative tools like median or quartiles for related summary statistics. The agent must infer that this is the tool for arbitrary percentile queries versus the fixed-percentile 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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