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MCPFax Public-Data Utility API

Business-day math

v1_business_days
Read-onlyIdempotent

Business-day math: Count business days in a range, or add/subtract N business days, skipping weekends & holidays. Source: computed + Nager.Date. $0.005 per call · GET /v1/business-days

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoEnd date (count mode). Example: '2026-01-31'.
daysNoOffset in business days (offset mode; may be negative). Example: '10'.
startYesStart date YYYY-MM-DD. Example: '2026-01-02'.
countryNoISO alpha-2 for holidays (default US). Example: 'US'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / country / examples
      Added value: +[
      +  "US"
      +]
    • addedInput schema / properties / days / examples
      Added value: +[
      +  10
      +]
    • addedInput schema / properties / end / examples
      Added value: +[
      +  "2026-01-31"
      +]
    • addedInput schema / properties / start / examples
      Added value: +[
      +  "2026-01-02"
      +]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive; the description adds useful non-obvious context: 'Source: computed + Nager.Date', '$0.005 per call', and 'GET /v1/business-days'. This informs an agent about data provenance, cost, and HTTP interface without contradicting 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.

Conciseness5/5

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

The description is compact and front-loaded: one sentence covering behavior, data source, cost, and endpoint path, with no filler. Every clause earns its place.

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 simple read-only calculator, the description plus the fully documented schema is mostly sufficient: required start, count vs offset modes, and country default are all covered. The main gaps are the return format and inclusive/exclusive boundary semantics, but these are minor given the examples and low complexity.

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?

The schema already documents all four parameters with examples and mode labels ('count mode', 'offset mode'), giving 100% coverage. The description restates the count/offset concepts but does not add parameter-level meaning beyond what the schema already provides, so it stays at the baseline of 3.

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 opens with 'Business-day math' and explicitly states the two operations: 'Count business days in a range, or add/subtract N business days, skipping weekends & holidays.' This clearly names the resource and behavior, making it easy to distinguish from siblings like v1_holidays or v1_timezone.

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

Usage Guidelines4/5

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

The description gives clear context for when to use the tool: any business-day calculation that skips weekends and holidays. It does not explicitly name alternatives or exclusion cases, such as pointing to v1_holidays for raw holiday data, so it stops short of full when-not guidance.

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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