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Dream Interpretation MCP Server by RoxyAPI

by com.roxyapi

Daily dream symbol - Dream symbol of the day API

post_dreams_daily
Read-only

Receive a single dream symbol for daily reflection and subconscious exploration. Uses seeded randomness so the same seed gets the same symbol on the same day, perfect for "Dream Symbol of the Day" features. Provide a seed (userId, email hash, session token) for reproducible consistency, or omit for date-based daily symbols. Returns the symbol with full psychological interpretation. Great for dream journal apps, wellness platforms, morning ritual apps, and meditation tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate for the reading in YYYY-MM-DD format. Defaults to today (UTC). Useful for viewing past daily readings or pre-generating future ones.
seedNoOptional seed for reproducible readings. Same seed + same date = same symbol every time. Pass any unique identifier (userId, email hash, session token). Omit for anonymous daily readings.
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. 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."
  2. 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."
  3. 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"
+}
  4. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Discloses the seeded randomness behavior, determinism on same seed and date, and that it returns full psychological interpretation. The readOnly and destructiveHint annotations already convey safety, and the description adds meaningful deterministic behavior without contradicting them.

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?

The description is compact, front-loaded with the core purpose, and every sentence adds useful context. The use-case sentence is slightly promotional but still informative.

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?

For a simple read-only endpoint with three optional parameters and no output schema, the description is nearly complete. It could more precisely describe the response fields beyond 'full psychological interpretation,' but nothing critical is missing for correct invocation.

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?

The schema already covers all three parameters, but the description adds practical meaning: seed can be a userId, email hash, or session token, and omitting seed gives date-based daily symbols. This goes beyond the schema's parameter docs.

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 clearly states the tool returns a single dream symbol for daily reflection, with a specific daily/seeded behavior. It does not explicitly name sibling tools or how it differs from them, especially get_dreams_symbols_random, so it falls short of full differentiation.

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 gives clear context for when this is appropriate: daily dream journal features, wellness apps, and meditation tools. It also explains the seed versus no-seed choice. However, it does not explicitly say when to use this instead of the sibling random symbol endpoint.

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

A4/5.0
Disambiguation4/5

Each tool has a clear role: search/list, detail by ID, letter counts, random, and daily seeded random. The only mild overlap is between get_dreams_symbols_random and post_dreams_daily, but deterministic seed/date behavior distinguishes the daily tool.

Naming Consistency4/5

Most tools follow a consistent get_dreams_symbols_* convention with clear suffixes for id, letters, and random. post_dreams_daily breaks the pattern by using post and daily, but it remains readable and predictable.

Tool Count5/5

Five tools is well-scoped for a dream interpretation dictionary API: browse/search, detail, alphabet navigation, random exploration, and a daily symbol feature. Each tool serves a distinct user need without bloat.

Completeness5/5

The tool surface covers the core read-only dictionary workflow completely: discovery, lookup, navigation, and engagement via random and daily symbols. No obvious missing operations are needed for this domain.

Resources