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Tarot MCP Server by RoxyAPI

Yes or no answer - Yes no tarot reading API

post_tarot_yes_no
Read-only

Ask a specific question and receive a yes, no, or maybe answer based on a single tarot card draw. Upright cards indicate "Yes" with positive energy, reversed cards indicate "No" with caution, and certain inherently ambiguous cards (The Hanged Man, Wheel of Fortune, Temperance, Two of Swords, Four of Swords) return "Maybe" regardless of orientation since their energy signals pause, reflection, or shifting circumstances. Major Arcana cards give strong definitive answers, Minor Arcana cards give qualified nuanced answers. Returns the answer, strength level, drawn card details, and a contextual interpretation explaining why. Perfect for decision-making apps, quick guidance tools, fortune-telling chatbots, and interactive tarot experiences. Optionally provide a seed for reproducible answers.

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
seedNoOptional seed for reproducible results. Same seed + same question = same answer. Useful for testing, sharing readings, or ensuring consistency. Omit for random draws each time.
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.
questionNoYour specific yes/no question. Be clear and focused. Good: "Should I move to a new city?" Bad: "What should I do about my life?" The more specific the question, the more useful the tarot guidance.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cardYes
seedNo
answerYes
questionNo
strengthYes
interpretationYes
answerLocalizedNo
strengthLocalizedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedOutput schema / properties / answerLocalized
      Added value: +{
      +  "type": "string"
      +}
    • addedOutput schema / properties / strengthLocalized
      Added value: +{
      +  "type": "string"
      +}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "answer": {
      +      "enum": [
      +        "Yes",
      +        "No",
      +        "Maybe"
      +      ],
      +      "type": "string"
      +    },
      +    "card": {
      +      "properties": {
      +        "arcana": {
      +          "enum": [
      +            "major",
      +            "minor"
      +          ],
      +          "type": "string"
      +        },
      +        "id": {
      +          "type": "string"
      +        },
      +        "imageUrl": {
      +          "type": "string"
      +        },
      +        "keywords": {
      +          "items": {
      +            "type": "string"
      +          },
      +          "type": "array"
      +        },
      +        "name": {
      +          "type": "string"
      +        },
      +        "reversed": {
      +          "type": "boolean"
      +        }
      +      },
      +      "required": [
      +        "id",
      +        "name",
      +        "arcana",
      +        "reversed",
      +        "keywords",
      +        "imageUrl"
      +      ],
      +      "type": "object"
      +    },
      +    "interpretation": {
      +      "type": "string"
      +    },
      +    "question": {
      +      "type": "string"
      +    },
      +    "seed": {
      +      "type": "string"
      +    },
      +    "strength": {
      +      "enum": [
      +        "Strong",
      +        "Qualified"
      +      ],
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "answer",
      +    "strength",
      +    "card",
      +    "interpretation"
      +  ],
      +  "type": "object"
      +}
  3. 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"
      +]
  4. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
      +  {}
      +]
    • 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."
  5. Changed1 schema field changed
    • changedInput schema / properties / seed / example
      Previous value: -"optional-seed"New value: +"reading-2f9c1a"
  6. 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."
  7. 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"
      +}
  8. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds real behavioral context beyond that: the upright/reversed mapping to Yes/No, the fixed set of ambiguous cards that return 'Maybe' regardless of orientation, Major vs Minor Arcana answer strength, and what fields are returned. It does not, however, address rate limits or failure behavior.

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 purpose is front-loaded and the core mechanic is explained efficiently. The 'Perfect for decision-making apps...' sentence is marketing filler that doesn't help an agent invoke the tool, and the answer-mechanics sentences are somewhat dense, but overall the text is well-organized and mostly earns its place.

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?

An output schema exists, annotations cover the safety profile, and the description still explains the answer logic, ambiguity handling, and return fields. Nothing an agent needs to select and call this tool correctly is missing.

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 all four parameters (lang, seed, compact, question) are already documented in the schema. The description only echoes the seed behavior ('Optionally provide a seed for reproducible answers') and adds no syntax or format detail beyond what the schema provides. Baseline 3 when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource with a distinctive output format: 'Ask a specific question and receive a yes, no, or maybe answer based on a single tarot card draw.' The yes/no/single-card framing inherently separates it from spread-reading siblings. However, it never names or contrasts any sibling (e.g. post_tarot_draw or post_tarot_spreads_*), so explicit sibling differentiation is absent.

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?

'Perfect for decision-making apps, quick guidance tools, fortune-telling chatbots...' describes audiences rather than when an agent should select this tool over alternatives. There is no explicit when-to-use/when-not-to-use or mention of the sibling tools. Usage is only implied by the use-case list.

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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