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Luissalet

Laplace's Hoard MCP Server

by Luissalet
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Python
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calc

Read-onlyIdempotent

Perform exact arithmetic, percentages, statistics, and conversions with high precision, returning citeable results for verification.

Instructions

Exact arithmetic. Never do arithmetic in your head: call this, even for "simple" sums.

Write Python-like syntax: + - * / // % ** (or ^), parentheses, comparisons. Functions: sqrt cbrt root(x, n) exp ln log(x, base) log10 log2, trig, floor ceil round(x, n) abs, min max sum mean median (numbers or one list), factorial binomial gcd lcm mod, isprime nextprime factorint (alone), and percentages: pct(15, 2347) = 15% of 2347, pct_change(old, new) = % change, ratio(a, b). Constants: pi e tau inf. Numbers are exact: 0.1 + 0.2 = 3/10. Examples: "pct(21, 1250)", "(1.05^10 - 1) * 100", "mean([3, 5, 8])". Returns {id, cite, exact, decimal (text, precision significant digits), is_exact (true when decimal is the exact value), latex}. No variables: use math for x, y. Cite the number as its cite, e.g. [L-000042].

Keywords: calculate, compute, how much is, percentage, percent of, discount, VAT, interest, average, square root, factorial, is prime, calcular, cuánto es, cuánto son, porcentaje, tanto por ciento, descuento, IVA, interés, media, raíz cuadrada, factorial, es primo.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
precisionNo
expressionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses that numbers are exact, gives the return format with cite/exact/decimal, and specifies that the cite should be used when referencing the result. This gives the agent important behavioral context not present in the annotations.

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?

The description is long but densely packed with useful information. It is front-loaded with the core directive, then organized into syntax, functions, return format, and keywords. Some keyword repetition could be trimmed, but the structure is clear and purposeful.

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?

Given the tool's high complexity and no output schema, the description is remarkably complete: it covers syntax, functions, constants, exactness semantics, return fields, limitations, and sibling differentiation. An agent has enough information to invoke it correctly and interpret its result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by specifying the expression syntax, supported operators, functions, constants, and example expressions. It also explains the role of precision in the decimal output, giving meaning to both parameters.

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 immediately states that this tool performs exact arithmetic and explicitly instructs the agent to call it even for simple sums. It also distinguishes itself from the sibling 'math' tool by noting that variables should be handled there.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance: never do arithmetic in your head, call this tool for any calculation. It also gives a clear exclusion and alternative, saying 'No variables: use math for x, y,' which routes the agent to the correct sibling.

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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