Memlord MCP Server

<h2 align="center">Self-hosted MCP memory server for personal use and teams</h4>

Remote serverstreamable-httpPython

What is the Memlord MCP server?

Most knowledge and memory work still happens through a UI a human drives. Memlord MCP server moves it into the conversation instead. Self-hosted MCP memory server for personal use and teams.

The tools it exposes

The server publishes 11 tools. What each one is for:

  • store_memory — Save a memory (idempotent by content); raises on near-duplicates; optional expires_at
  • retrieve_memory — Hybrid semantic + full-text search; returns snippets by default
  • recall_memory — Search by natural-language time expression; returns snippets by default
  • list_memories — Paginated list with type/tag filters
  • search_by_tag — AND/OR tag search
  • get_memory — Fetch a single memory by name with full content (expired included)
  • update_memory — Update content, type, tags, metadata, or expiry by name (and optionally rename)
  • delete_memory — Delete by name
  • move_memory — Move a memory to a different workspace
  • list_workspaces — List workspaces you are a member of (including personal)
  • dream_report — Read-only consolidation candidates: similar memory pairs, expired and expiring-soon memories

Getting it running

Because this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.

What it needs from you

Configuration is passed through the environment: MEMLORD_DB_URL, MEMLORD_BASE_URL, MEMLORD_OAUTH_JWT_SECRET. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.

How it compares

Among the knowledge and memory options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Memlord's toolset — store_memory, retrieve_memory, recall_memory and 8 more — is a fair guide to whether it matches your workflow. It is maintained by myrikld; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Memlord's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Things to watch

  • Your data travels to the provider's service, so the usual questions apply about what you send and what they retain.
  • With 11 tools registered it takes up a noticeable share of the context window; turn it off in projects that never touch Memlord.
  • Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
  • Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.

Available tools

ToolWhat it does
store_memorySave a memory (idempotent by content); raises on near-duplicates; optional expires_at
retrieve_memoryHybrid semantic + full-text search; returns snippets by default
recall_memorySearch by natural-language time expression; returns snippets by default
list_memoriesPaginated list with type/tag filters
search_by_tagAND/OR tag search
get_memoryFetch a single memory by name with full content (expired included)
update_memoryUpdate content, type, tags, metadata, or expiry by name (and optionally rename)
delete_memoryDelete by name
move_memoryMove a memory to a different workspace
list_workspacesList workspaces you are a member of (including personal)
dream_reportRead-only consolidation candidates: similar memory pairs, expired and expiring-soon memories

Configuration

VariableDescriptionRequired
MEMLORD_DB_URLEndpoint or connection string the server talks to.Yes
MEMLORD_BASE_URLEndpoint or connection string the server talks to.Yes
MEMLORD_OAUTH_JWT_SECRETCredential the server authenticates with.Yes

Example prompts to try

  • Use Memlord to store memory.
  • Use Memlord to retrieve memory.
  • Use Memlord to recall memory.

Frequently asked questions

It connects Memlord to MCP-compatible AI assistants such as Claude and Cursor, exposing 11 tools (store_memory, retrieve_memory, recall_memory, and more) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Memlord directly.