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Western Astrology MCP Server by RoxyAPI

by com.roxyapi

Calculate planetary aspects - Aspect finder for any date and time

post_astrology_aspects
Read-only

Calculate all major and minor aspects between planets for any date and time. Finds conjunctions (0°), oppositions (180°), trines (120°), squares (90°), sextiles (60°), and minor aspects. Returns aspect type, exact angle, orb, applying/separating status, and strength (0-100). Filter by specific planets or aspect types. Perfect for aspect tables, transit analysis, and aspect pattern detection. Uses standard Western astrology orbs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesDate in YYYY-MM-DD format
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
timeYesTime in HH:MM:SS format (24-hour)
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.
planetsNoOptional: specific bodies to calculate aspects for (defaults to all 14: the 10 classical planets, the lunar nodes, Chiron, and Black Moon Lilith)
timezoneYesTimezone offset from UTC in decimal hours (NOT minutes format). Examples: New York EST = -5, India IST = 5.5 (NOT 5:30), Tokyo JST = 9. IMPORTANT: Use decimal format (5.5, not 5:30).
aspectTypesNoOptional: specific aspect types to find (defaults to all 9)

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / properties / lang / description
      Previous value: -"Response language (ISO 639-1). Supported: en, tr, de, es, hi, pt, fr, ru. Defaults to en. Languages without translations yet return English."New value: +"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."
    • changedInput schema / properties / lang / enum
      Previous value: -[
-  "en",
-  "tr",
-  "de",
-  "es",
-  "hi",
-  "pt",
-  "fr",
-  "ru"
-]New value: +[
+  "en",
+  "tr",
+  "de",
+  "es",
+  "hi",
+  "pt",
+  "fr",
+  "ru",
+  "zh-Hans",
+  "zh-Hant"
+]
  2. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "date": "1990-07-15",
+    "time": "14:30:00",
+    "timezone": -5
+  }
+]
    • changedInput schema / properties / compact / description
      Previous value: -"Set true to receive the exact same data in a token-optimized shape that is cheaper for you to read: whitespace is stripped and every array of same-shaped objects is encoded columnar as {\"__cols\":[field names],\"__rows\":[[values]]}, so each field name is sent once instead of once per row. Fully lossless (no field or value is dropped or changed) and typically 40 to 52 percent fewer tokens on large results. Prefer true whenever token or inference cost matters. Default false returns standard indented JSON."New value: +"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."
  3. Changed1 schema field changed
    • changedInput schema / properties / compact / description
      Previous value: -"Return the same data in a token-optimized compact shape (minified, with same-shaped arrays encoded columnar) to reduce LLM token cost. Lossless: no fields are dropped. Default false."New value: +"Set true to receive the exact same data in a token-optimized shape that is cheaper for you to read: whitespace is stripped and every array of same-shaped objects is encoded columnar as {\"__cols\":[field names],\"__rows\":[[values]]}, so each field name is sent once instead of once per row. Fully lossless (no field or value is dropped or changed) and typically 40 to 52 percent fewer tokens on large results. Prefer true whenever token or inference cost matters. Default false returns standard indented JSON."
  4. Changed1 schema field changed
    • addedInput schema / properties / compact
      Added value: +{
+  "default": false,
+  "description": "Return the same data in a token-optimized compact shape (minified, with same-shaped arrays encoded columnar) to reduce LLM token cost. Lossless: no fields are dropped. Default false.",
+  "type": "boolean"
+}
  5. Changed1 schema field changed
    • changedInput schema / properties / timezone / anyOf
      Previous value: -[
-  {
-    "maximum": 14,
-    "minimum": -14,
-    "type": "number"
-  },
-  {
-    "pattern": "^[A-Za-z_]+(?:\\/[A-Za-z0-9_+-]+){0,2}$",
-    "type": "string"
-  }
-]New value: +[
+  {
+    "maximum": 14,
+    "minimum": -14,
+    "type": "number"
+  },
+  {
+    "type": "string"
+  }
+]
  6. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description goes beyond the read-only annotations by revealing the output shape: aspect type, exact angle, orb, applying/separating status, and strength (0-100). It also discloses the calculation basis ('standard Western astrology orbs'). This gives an agent a clear behavioral model without hidden surprises.

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 most discriminating detail—'any date and time'—is front-loaded, and every following sentence contributes either an example aspect, output fields, filter capability, or use cases. It could be tightened slightly by cutting the partially redundant 'Finds conjunctions...' sentence, but it is still well shaped.

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?

Despite no output schema, the description covers primary returned fields and the orb standard, and the schema carries the parameter constraints. The main missing precision is the full list of minor aspects and an explicit routing note that differentiates e.g., aspects_monthly or transit_aspect from this call, so it is solid but not maximally complete.

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?

Since the schema covers 100% of the parameters with detailed examples and typical descriptions (date format, decimal timezone, language enum, compact shape), the description largely does not need to add parameter-level meaning. It only glosses the filters and does not introduce new parameter details.

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 says 'Calculate all major and minor aspects between planets for any date and time,' naming a precise action, resource, and temporal scope. The list of aspect degrees makes the tool immediately recognizable and differentiates it from the monthly aspect and pattern detection siblings.

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?

It provides clear context by anchoring the tool to a user-supplied date/time and names typical use cases such as aspect tables and transit analysis. It does not explicitly say 'use X instead when…', so it falls just short of the explicit exclusion bar.

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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TDQS

A3.8/5.0
Disambiguation3/5

Most tools are scoped to a clearly distinct domain—moon phases, houses, returns, fixed stars, astrocartography—but several pairs overlap, notably synastry vs. compatibility_score and transits vs. transit_aspects. The descriptions are long and do help an agent choose, but the boundaries are not always obvious at the name level.

Naming Consistency4/5

Every tool follows the same snake_case get_astrology_/post_astrology_ prefix pattern, making the group predictable and readable. However, collection endpoints like get_astrology_planet_meanings_id and get_astrology_signs_id awkwardly combine plural collection and singular id naming, and names like calendar_year_month depart from the cleaner resource+suffix pattern.

Tool Count2/5

With 38 tools, this is well over the 25+ threshold and feels heavy for an agent to traverse. While Western astrology is a broad domain, several near-duplicate endpoints, especially synastry/compatibility and transits/transit_aspects, could be consolidated into fewer, more scoped tools.

Completeness5/5

The toolset is comprehensive, covering natal charts, relationships, transits, returns, progressions, aspects, houses, relocation, and reference data. It includes enough specialized endpoints for both casual horoscope use and in-depth astrological analysis, with no obvious dead ends for the stated domain.

Resources