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Mesoamerican Astrology MCP Server by RoxyAPI

Get one Mayan trecena - Thirteen day period profile API

get_mesoamerican_astrology_mayan_trecenas_number
Read-only

Retrieve one of the twenty trecenas by number, with the day sign it opens on in both naming traditions and a composed reading of the period. Built for period detail pages and for apps that show which thirteen day block a date falls in.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoResponse language (BCP 47). Supported: en, tr, de, es, hi, pt, fr, ru, zh-Hans, zh-Hant. Defaults to en. Coverage varies by domain, and a field with no translation in the requested language returns English.en
numberYesTrecena number, 1 to 20. Trecena 1 opens on 1 Imix, trecena 2 on 1 Ix, and each subsequent period opens thirteen signs further round the twenty.
compactNoSet true for the same data in a compact shape: arrays of same-shaped objects arrive columnar as {"__cols":[names],"__rows":[[values]]}. Lossless, typically 40 to 52 percent fewer tokens.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful context about the response content, but does not disclose behavior for invalid numbers, missing translations, or the exact response structure. This is acceptable given the annotations but not exceptional.

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?

Two sentences with no filler: the first states the action and output, the second provides use-case context. Every phrase earns its place and the core information is front-loaded.

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 there is no output schema, the description does indicate what the response contains: the opening day sign in both naming traditions and a composed reading. The parameter documentation is thorough. Minor gaps like exact field names or out-of-range behavior do not prevent a correct call.

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?

Input schema coverage is 100% and each parameter has a detailed description, including the number range, language fallback behavior, and compact output shape. The tool description adds little beyond 'by number,' so the schema carries the semantic weight—baseline 3 is appropriate.

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 a specific verb and resource: 'Retrieve one of the twenty trecenas by number.' It also names the key returned content—opening day sign in both naming traditions and a composed reading—so the tool's purpose is unambiguous and distinguishable from the sibling list endpoint.

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 concrete intended contexts: 'Built for period detail pages and for apps that show which thirteen day block a date falls in.' It does not explicitly name alternatives or state when not to use it, but the use cases are clear enough for an agent to route appropriately.

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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