Mesoamerican Astrology MCP Server by RoxyAPI

List the 20 Aztec day signs - Tonalpohualli sign catalogue API

get_mesoamerican_astrology_aztec_day_signs

Read-only

Retrieve all twenty tonalpohualli day signs in sequence order from Cipactli to Xochitl. Each entry returns the machine id, the Nahuatl name, what it means, the world direction it belongs to and a composed reading. Built for Aztec sign pickers, Mexica calendar reference pages and any app that needs the whole set in one call.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lang`	No	Response 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
`limit`	No	Maximum items to return per page. Range: 1-20, default 20.
`offset`	No	Number of items to skip for pagination. Default 0.
`compact`	No	Set 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.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	Yes
`total`	Yes
`offset`	Yes
`daySigns`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 23, 2026
- addedInput schema / properties / offset / default
```
Added value: +0
```
- addedInput schema / properties / offset / minimum
```
Added value: +0
```
Changed3 schema fields changedSep 20, 2026
- removedInput schema / properties / offset / default
```
Removed value: -0
```
- removedInput schema / properties / offset / minimum
```
Removed value: -0
```
- changedInput schema / properties / offset / type
```
Previous value: -[
-  "integer",
-  "null"
-]New value: +"integer"
```

Changed1 schema field changedSep 20, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "daySigns": {
+      "items": {
+        "properties": {
+          "direction": {
+            "type": "string"
+          },
+          "essence": {
+            "type": "string"
+          },
+          "gloss": {
+            "type": "string"
+          },
+          "guidance": {
+            "type": "string"
+          },
+          "id": {
+            "type": "string"
+          },
+          "keynote": {
+            "type": "string"
+          },
+          "nameNahuatl": {
+            "type": "string"
+          },
+          "position": {
+            "type": "number"
+          }
+        },
+        "required": [
+          "position",
+          "id",
+          "nameNahuatl",
+          "gloss",
+          "direction",
+          "essence",
+          "guidance",
+          "keynote"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "limit": {
+      "type": "number"
+    },
+    "offset": {
+      "type": "number"
+    },
+    "total": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "total",
+    "limit",
+    "offset",
+    "daySigns"
+  ],
+  "type": "object"
+}

First observedSep 6, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavioral context: results are returned in sequence order and each entry includes a machine id, Nahuatl name, meaning, world direction, and composed reading. It doesn't disclose pagination or language fallback behavior, but those are already documented in the schema.

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 dense, front-loaded sentences with no filler. The core behavior, ordering, and returned fields appear immediately, and the use-case sentence earns its place by clarifying when the tool is the right choice.

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 rich schema, output schema, and safety annotations, the description covers ordering, scope, returned fields, and intended use cases. There is nothing material missing for an agent to select and invoke this tool correctly.

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 description coverage is 100%, so the schema fully documents lang, limit, offset, and compact. The description adds no additional parameter-level meaning beyond mentioning the full set and returned fields, so the baseline of 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 specifies a clear verb and resource: 'Retrieve all twenty tonalpohualli day signs in sequence order from Cipactli to Xochitl.' It also clarifies the data returned per entry and distinguishes itself from sibling tools by positioning this as the 'whole set in one call' 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 clear usage context: it is built for Aztec sign pickers, Mexica calendar reference pages, and any app needing the complete set in one call. It does not explicitly name alternatives or exclusions, but the 'whole set in one call' phrasing implicitly routes users away from the per-id or Mayan sibling tools.

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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