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Searchable catalog of Model Context Protocol servers. Find tools that connect your AI assistant to the services and data you use.

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ยฉ 2026 MCPHub

  1. Servers
  2. /Databases
  3. /Memlord
DatabasesOfficial
M

Memlord

  • ๐Ÿ” Hybrid search โ€” BM25 (full-text) + vector KNN (pgvector) fused via Reciprocal Rank Fusion - ๐Ÿ“‚ Multi-user โ€” each user sees only their own memories; workspaces for shared team knowledge - ๐Ÿ› ๏ธ 11 MCP tools โ€” store, retrieve, recall, list, search by tag, get, update, delete, move, list workspaces, dream report - ๐Ÿ’ค Dreaming โ€” a guided consolidation pass (dream MCP prompt + dream_report tool): finds near-duplicate and conflicting memories, merges them into insights non-destructively, driven by the client LLM - ๐ŸŒ Web UI โ€” browse, search, edit and delete memories in the browser; export/import JSON - ๐Ÿ”’ OAuth 2.1 โ€” full in-process authorization server, always enabled - ๐Ÿ˜ PostgreSQL โ€” pgvector for embeddings, tsvector for full-text search - ๐Ÿ“Š Progressive disclosure โ€” search returns compact snippets by default; call get_memory(name) only for what you need, reducing token usage - ๐Ÿ” Deduplication โ€” automatically detects near-identical memories before saving, preventing noise accumulation
27 starsUpdated Aug 30, 2026
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Overview

  • ๐Ÿ” Hybrid search โ€” BM25 (full-text) + vector KNN (pgvector) fused via Reciprocal Rank Fusion - ๐Ÿ“‚ Multi-user โ€” each user sees only their own memories; workspaces for shared team knowledge - ๐Ÿ› ๏ธ 11 MCP tools โ€” store, retrieve, recall, list, search by tag, get, update, delete, move, list workspaces, dream report - ๐Ÿ’ค Dreaming โ€” a guided consolidation pass (dream MCP prompt + dream_report tool): finds near-duplicate and conflicting memories, merges them into insights non-destructively, driven by the client LLM - ๐ŸŒ Web UI โ€” browse, search, edit and delete memories in the browser; export/import JSON - ๐Ÿ”’ OAuth 2.1 โ€” full in-process authorization server, always enabled - ๐Ÿ˜ PostgreSQL โ€” pgvector for embeddings, tsvector for full-text search - ๐Ÿ“Š Progressive disclosure โ€” search returns compact snippets by default; call get_memory(name) only for what you need, reducing token usage - ๐Ÿ” Deduplication โ€” automatically detects near-identical memories before saving, preventing noise accumulation

Capabilities

  • store_memory

    Store a memory with type and tags โ€” Use `store_memory` to save a fact, preference, instruction, feedback, decision, or insight, optionally setting an expiration.

  • retrieve_memory

    Search memories with hybrid retrieval โ€” Ask `retrieve_memory` to find relevant memories using combined BM25 full-text and vector semantic search, returning compact snippets.

  • recall_memory

    Recall memories by time expression โ€” Use `recall_memory` to find memories matching natural-language time queries like "last week" or "yesterday."

  • list_memories

    Browse and filter all memories โ€” Call `list_memories` with type and tag filters to paginate through stored memories.

  • dream

    Consolidate memories via dreaming โ€” Run the `dream` prompt to have the assistant review `dream_report` candidates, merge near-duplicates into insights, and retire superseded memories non-destructively.

Installation

docker compose up

Configuration

{
  "mcpServers": {
    "memlord": {
      "url": "https://img.shields.io/badge/MCP"
    }
  }
}
Add to Cursor

Paste into ~/.cursor/mcp.json or .cursor/mcp.json

Tags

  • memory
  • knowledge-retrieval
  • database
  • collaboration

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