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CALCULATE_MCP

CALCULATE_MCP

by YAMA-TANA
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

summarize_statistics

Compute count, sum, mean, median, range, variance, standard deviation, quartiles, and IQR from numeric values to analyze dataset distribution.

Instructions

Compute descriptive statistics: count, sum, mean, median, range, variance, standard deviation, quartiles, and IQR.

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": {
      "values": "<values>"
    },
    "name": "summarize_statistics"
  }
}

Input Schema

NameRequiredDescriptionDefault
valuesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

B3.4/5.0
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 burden. It usefully discloses what is computed (count, sum, mean, ...), which is the primary behavior for a stateless pure computation, but says nothing about the 100,000-element cap, behavior on empty/NaN input, or the shape/order of results.

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 the verb first and the enumeration of outputs following. Every element earns its place because there is no output schema to explain the return values.

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?

Given a very simple one-parameter tool with no output schema, the description lists the computed statistics, which is the key missing structured information. It is nearly complete, lacking only input-constraint and return-format details.

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?

One parameter ('values') exists with 0% schema description coverage, so the description ideally should clarify input expectations. It implies the input is a set of numbers being summarized but never names or constrains the parameter; the maxItems=100000 and minItems=1 limits are only in the schema.

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 ('Compute descriptive statistics') and enumerates the exact outputs, so the agent knows what it produces. It does not, however, differentiate itself from the numerous sibling calculators (calculate_probability, calculate_expression, etc.), which all involve numbers.

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 statement of when to use this tool versus the sibling calculators, nor any prerequisites or exclusions. The agent must infer that a numeric array should be summarized here rather than processed by calculate_expression.

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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