mempalace memory

https://mempalace-mcp.vercel.app/ — Register or sign in here first to create your API key (mpk...).

От сообществаРаботаетНужен API-ключГлобальныйБесплатноМожет изменять данные

Что умеет

  • Mempalace Status: Summarize the current user's memory palace: total drawers, counts per wing, counts per room. Use when you need grounding before writes or searches, or when the user asks what is stor
  • Mempalace Add Drawer: Persist a new memory drawer: writes to the user's D1 row, then embeds and upserts to Vectorize for later semantic search. Use when the user wants to remember something long-term
  • Mempalace Search: Search the current user's memories by meaning (Vectorize embeddings), optionally scoped to wing and/or room. Use when answering questions that depend on prior stored context, or befo

Какие данные видит

Нужен ли аккаунт

Нужен API-ключ из настроек сервиса

https://mempalace-mcp.vercel.app/ — Register or sign in here first to create your API key (mpk_...).

mempalace memory is the hosted MCP layer from the mempalace (Memory Palace) project: per-user memory drawers (wing/room), semantic search on Cloudflare (D1 + Vectorize + Workers AI embeddings). MCP endpoint: https://glidepath.dpdns.org/mcp . Authenticate with Authorization: Bearer or X-Mempalace-Api-Key (for Smithery Session Config / gateways). MIT license. In-server docs: MCP resources under docs://mempalace-memory/* (getting-started, authentication, tools-reference, data-model, troubleshooting).

Список инструментов сервера (3)

Технические названия из tools/list. Нужны только разработчикам.

mempalace_statusSummarize the current user's memory palace: total drawers, counts per wing, counts per room. Use when you need grounding before writes or searches, or when the user asks what is stored. Do not use for semantic lookup—use mempalace_search instead. Read-only. Returns JSON in MCP text: { total_drawers, wings: [{ wing, c }], rooms: [{ room, c }] }.
mempalace_add_drawerPersist a new memory drawer: writes to the user's D1 row, then embeds and upserts to Vectorize for later semantic search. Use when the user wants to remember something long-term (facts, decisions, contacts, meeting notes, checkpoints). Do not use to retrieve data—use mempalace_search. Not idempotent: each call creates a new row. Response JSON: { success, memory_id, wing, room, vectorize: { ok, len?, error? } }; vectorize.ok may be false while the row still saved.
mempalace_searchSearch the current user's memories by meaning (Vectorize embeddings), optionally scoped to wing and/or room. Use when answering questions that depend on prior stored context, or before writing to avoid duplicates. Read-only. If the index is still catching up after a recent write, mode may be like_fallback (SQL substring). Response JSON: { query, results: [{ id, text, wing, room, source_file, similarity }], filters: { wing, room }, mode: 'vectorize' | 'like_fallback' }.