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Numerology MCP Server by RoxyAPI

Calculate numerology compatibility - Love match scoring API

post_numerology_compatibility
Read-only

Calculate numerology compatibility between two people from their Life Path, Expression and Soul Urge numbers. Send each person as a full birth name with a birth date, or as precomputed numbers, and mix the two modes freely across the pair. The response returns an overall score from 50 to 100 with its rating, a score and description for each number pair, and the strengths, challenges and advice for the relationship, in the language set by lang. Built for dating apps, matchmaking, relationship coaching and AI companions.

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
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.
person1Yes
person2Yes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
adviceYes
ratingYes
lifePathYes
soulUrgeYes
strengthsYes
challengesYes
expressionYes
overallScoreYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "advice": {
      +      "type": "string"
      +    },
      +    "challenges": {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    "expression": {
      +      "properties": {
      +        "compatibility": {
      +          "type": "number"
      +        },
      +        "description": {
      +          "type": "string"
      +        },
      +        "person1": {
      +          "type": "number"
      +        },
      +        "person2": {
      +          "type": "number"
      +        }
      +      },
      +      "required": [
      +        "person1",
      +        "person2",
      +        "compatibility",
      +        "description"
      +      ],
      +      "type": "object"
      +    },
      +    "lifePath": {
      +      "properties": {
      +        "compatibility": {
      +          "type": "number"
      +        },
      +        "description": {
      +          "type": "string"
      +        },
      +        "person1": {
      +          "type": "number"
      +        },
      +        "person2": {
      +          "type": "number"
      +        }
      +      },
      +      "required": [
      +        "person1",
      +        "person2",
      +        "compatibility",
      +        "description"
      +      ],
      +      "type": "object"
      +    },
      +    "overallScore": {
      +      "type": "number"
      +    },
      +    "rating": {
      +      "type": "string"
      +    },
      +    "soulUrge": {
      +      "properties": {
      +        "compatibility": {
      +          "type": "number"
      +        },
      +        "description": {
      +          "type": "string"
      +        },
      +        "person1": {
      +          "type": "number"
      +        },
      +        "person2": {
      +          "type": "number"
      +        }
      +      },
      +      "required": [
      +        "person1",
      +        "person2",
      +        "compatibility",
      +        "description"
      +      ],
      +      "type": "object"
      +    },
      +    "strengths": {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    }
      +  },
      +  "required": [
      +    "overallScore",
      +    "rating",
      +    "lifePath",
      +    "expression",
      +    "soulUrge",
      +    "strengths",
      +    "challenges",
      +    "advice"
      +  ],
      +  "type": "object"
      +}
  2. 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"
      +]
  3. Changed1 schema field changed
    • 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."
  4. 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."
  5. 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"
      +}
  6. First observed

TDQS

A3.9/5.0
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 agent knows this is a side-effect-free computation. The description adds the score range (50-100), the rating, per-pair breakdown and strengths/challenges/advice, but much of that overlaps the output schema and no limits, latency, or failure modes are mentioned.

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?

Three sentences, front-loaded with the core computation before input modes and output shape. The trailing audience sentence ('Built for dating apps, matchmaking...') is mild positioning filler but still aids routing, so only minor trimming is warranted.

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?

An output schema exists so return values need not be re-explained, annotations cover the safety profile, and the description covers input modes, mixing behavior, scoring range and language handling for a nested two-object parameter set. The unmentioned compact flag is the only real gap.

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

Parameters4/5

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

With only 50% schema description coverage, the description compensates by mapping the input modes directly to parameters: name-based input yields Expression and Soul Urge, birth-date input yields Life Path, and the two can be mixed per person. It also confirms lang controls output language, though the compact parameter is never addressed.

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?

States a specific verb and resource ('Calculate numerology compatibility between two people') and names the exact inputs (Life Path, Expression, Soul Urge). This is clearly distinguishable from the single-number siblings like post_numerology_life_path or post_numerology_chart, since compatibility is a pairwise operation.

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

Usage Guidelines3/5

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

The description explains the two invocation modes ('full birth name with a birth date, or as precomputed numbers, and mix the two modes freely'), which is useful input guidance, and lists target audiences. However, it never states when to choose this tool over alternatives such as post_numerology_dual or the individual-number endpoints, so alternative selection is left implicit.

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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