MCP server

MindDory Brain Official

com.minddory/brain

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

Free: turn your AI chats into spaced-repetition vocabulary. 13 tools, reads and writes.

Website Source

At a glance

Type

MCP server

Publisher

minddory.com (verified by the MCP Registry)

Status

Working at last check

Tools

13

Token cost

About 3,702 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http brain https://api.minddory.com/v1/brain/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.brain]
url = "https://api.minddory.com/v1/brain/mcp"

Gemini CLI

{"mcpServers":{"brain":{"httpUrl":"https://api.minddory.com/v1/brain/mcp"}}}

Cursor

{"mcpServers":{"brain":{"url":"https://api.minddory.com/v1/brain/mcp"}}}

VS Code

{"servers":{"brain":{"type":"http","url":"https://api.minddory.com/v1/brain/mcp"}}}

Antigravity

agy mcp add brain https://api.minddory.com/v1/brain/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"brain","server_url":"https://api.minddory.com/v1/brain/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"brain","server_url":"https://api.minddory.com/v1/brain/mcp"}

Tools

• capture_grammar_mistake ~648 tokens
  Log a grammar mistake the user just made in the target language, creating a grammar point in their Minddory deck. Use it proactively whenever the user writes a target-language sentence with a clear, correctable error (wrong tense, agreement, word_choice, word_order, missing or wrong article, preposi
• capture_word ~704 tokens
  Capture a target-language word or phrase to the user's Minddory vocabulary deck (a flashcard in the "Chat Discoveries" folder when the word is new, otherwise a context encounter). The user connected Minddory so their assistant builds their deck from real conversations, so use this proactively to log
• check_words ~501 tokens
  Batch lookup: for a list of target-language words, tell me which ones are already in the user's Minddory deck and how well they know each. Use this BEFORE glossing or capturing vocabulary from a message - it answers "which of these are actually new to this user" in one call, so you can skip words th
• get_active_vocab ~245 tokens
  Get the user's most actively encountered target-language words (from past capture_word + log_interaction events), ranked by frequency over a lookback window. Use to surface "frontier" words the user keeps touching when they ask "what should I learn next" or when you want context-aware suggestions.
• get_card ~134 tokens
  Single card detail by word (case-insensitive). Returns translation, mastery, and last 10 events.
• get_known_words ~136 tokens
  Words the user has verified known via flashcard practice. Paginated keyset on flashcards.id.
• get_queue ~113 tokens
  Cards due now and due within the next 24 hours.
• get_recent_activity ~106 tokens
  Event log slice with optional surface filter and keyset pagination on answers.id.
• get_system_instructions ~313 tokens
  Call this at the START of every new conversation, before your first reply, to load the user's Minddory setup and your role as their proactive language partner: CEFR level, target/source languages, due-card count, weak words, and how to capture. The user connected Minddory to actively improve their l
• get_user_profile ~160 tokens
  Profile snapshot: CEFR level, target/source languages, due card count, weak words, recent lookups. Pass `lang` to scope the snapshot to one learning language (for users learning several); omit it for the user's primary language.
• log_interaction ~208 tokens
  Append a generic interaction event to the answers log. Use for lookups, AI discussions, and reading-in-context signals.
• mark_demonstrated ~234 tokens
  Confidence-weighted spaced-repetition boost when the user has used a word correctly: the card moves further out in the review schedule. Logs an answer row even if no flashcard exists.
• mark_struggled ~200 tokens
  Spaced-repetition degrade for a word the user just got wrong: the card comes back sooner. ease_factor drops, interval resets, repetitions reset.

Browse

• Translation

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