Best memory MCP server, and how to set one up

Why doesn’t my agent call its memory tools?#

Four causes, each from the makers’ own docs or Anthropic’s tracker:

1. The model decides. A memory server hands over tools and Claude chooses when to use them. Mem0 describes its server that way: it “hands your agent a set of memory tools, so it can decide for itself when to save something, look something up”. VS Code’s troubleshooting page lists “An MCP tool is not being invoked” and suggests you “State the tool name and the action it should perform in your prompt”.
2. Claude Code loads MCP tools on demand. “Tool search is enabled by default: MCP tools are deferred and discovered on demand”, and “Only tool names and server instructions load at session start” (Claude Code MCP docs). Claude has to think of searching for a memory tool before it can call one.
3. claude.ai and Claude Desktop’s custom connectors drop server instructions. An open issue on Anthropic’s claude-ai-mcp tracker reports that the instructions a server sends never reach the model. One user tested it with a hidden word: “claude.ai drops instructions; it does not truncate them”, while Claude Code returned it. In October two more reports said the same of Claude Desktop’s custom connectors. One later comment saw it only with connectors an organization added. These are users’ reports; no one from Anthropic has replied in the thread.
4. Something else gets there first. claude.ai has its own memory, and by Mem0’s docs, “Claude’s system prompt tends to favor its built-in memory”. In Claude Desktop, each tool asks permission until you allow it; Counterparts’ setup notes say that until a tool may run, “the model does not call one nobody asked for”.

What fixes it, by client:

The servers side by side#

Every row was read from its maker’s docs, README or source on 4 November 2026, and is sorted by what brings memory in. “Hook” means a Claude Code hook unless the row says otherwise. Each row gives defaults.

Hosted servers whose code isn’t public (Mem0’s, Zep Context, Basic Memory Cloud) are described from their docs.

Two more come up in searches. Letta’s hosted MCP server runs Letta agents for you; its tools don’t read or write your own memory. Screenpipe’s MCP server searches recordings of your screen, which is a different job.

Hooks exist in Claude Code, Cursor, Codex and a few other coding agents, and not in Claude Desktop’s chat or claude.ai. So the same server can bring memory in by itself in one client and wait for a tool call in another. And the remote servers, the only kind claude.ai can reach, keep your notes in their maker’s cloud; whatever Claude reads still goes to Anthropic as usual.

Our directory has 21 memory systems, each graded on the same 11 mechanisms of human memory; this table has the ones whose makers’ docs give them an MCP route to memory, plus mcp-memory-service, which isn’t in the directory. It leaves out agent-qa, a test harness.

How to set one up#

Claude Code

Counterparts (ours), Mac or Linux. In Claude Code:

/plugin marketplace add mlapeter/counterparts
/plugin install counterparts@counterparts

Restart Claude Code. The first session offers a short getting-to-know-you. Or from a terminal with bun 1.3+ or Node 22.15+: bun add -g counterparts (or npm install -g counterparts@latest), then counterparts, give your name, let it connect Claude Code, restart, and run counterparts doctor. It runs beside CLAUDE.md and Claude Code’s auto memory and doesn’t touch their files. The Claude Code page has the rest, and our guide to Claude Code memory plugins compares it with the other plugins.

Any other server. Claude Code’s command is claude mcp add <name> -- <command> for a local server and claude mcp add --transport http <name> <url> for a remote one. It saves to the current project unless you add --scope user (every project) or --scope project (a shared .mcp.json). For the reference server, with its file somewhere safe (make the folder first; in our test, a missing folder made every save fail with “no such file or directory”):

mkdir -p ~/.mcp-memory
claude mcp add memory --scope user --env MEMORY_FILE_PATH=$HOME/.mcp-memory/memory.jsonl -- npx -y @modelcontextprotocol/server-memory

Check it with /mcp. A tool-only server is the case where "alwaysLoad": true helps. Add it to the server’s entry in ~/.claude.json (local and user scope) or .mcp.json (project scope), or add the server with it in one line, in place of the add line above (or run claude mcp remove memory --scope user first):

claude mcp add-json memory --scope user '{"type":"stdio","command":"npx","args":["-y","@modelcontextprotocol/server-memory"],"env":{"MEMORY_FILE_PATH":"~/.mcp-memory/memory.jsonl"},"alwaysLoad":true}'

With it, the server’s tools “load into context regardless of the ENABLE_TOOL_SEARCH setting”. Add one line to CLAUDE.md as well, such as “Search memory before answering about past work”.

Claude Desktop

Counterparts (ours), Mac. From a terminal, install the command with bun add -g counterparts (or npm install -g counterparts@latest with Node 22.15+); the Claude Code plugin doesn’t put a counterparts command on your PATH. Quit Claude Desktop, run counterparts install --host claude-desktop --budget 9000, and reopen Desktop. It uses the same memory as Claude Code, in ~/.counterparts. Then set the counterparts tools to Always allow, or a chat can’t wake its memory on its own, and if the chat’s + › Connectors menu shows Tool access, set it to Always available in each chat. Our server sends Desktop instructions to call wake first, and offers a “Start with Counterparts” prompt that asks Claude to wake its memory before anything else. Desktop is reported to drop instructions from custom connectors (untested for local servers like this one). The wake tool’s own description also tells Claude to call it first in a new chat, and tool descriptions do reach the model in Desktop. Nobody has tested how often Claude does that unasked, so the prompt is the sure way in.

Any local server. Open claude_desktop_config.json directly (on a Mac in ~/Library/Application Support/Claude/, on Windows in %APPDATA%\Claude\), or from Desktop: Settings › Developer › Edit Config in the older Settings layout (in the newer one, Settings › Desktop app › Extensions (Manage) › Local MCP servers shows each server’s connection status and logs). Add the server under mcpServers:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"],
      "env": { "MEMORY_FILE_PATH": "/Users/you/.mcp-memory/memory.jsonl" }
    }
  }
}

Use your own full path there, and create the folder first (mkdir -p ~/.mcp-memory). Quit Desktop completely and reopen it; the MCP project’s guide says it “needs to restart to load the new configuration”. If the server doesn’t show up, its log is ~/Library/Logs/Claude/mcp-server-memory.log on a Mac. Servers packed as Desktop extensions (.mcpb) install from Settings › Desktop app › Extensions instead. Remote servers go in as connectors, the same way as claude.ai.

claude.ai (web and apps)

claude.ai reaches only remote servers, over the public internet: Anthropic’s cloud connects to the server, “rather than from your local device”, and servers in Desktop’s config file “aren’t available in Cowork or claude.ai” (Anthropic’s help). Custom connectors are on every plan; Free allows one, and on Team and Enterprise an Owner adds them.

1. Go to Customize › Connectors, click + Add, then Add custom connector.
2. Paste the server’s address and sign in to its service when asked. Addresses from the makers’ docs: Basic Memory Cloud https://cloud.basicmemory.com/mcp (also listed in Claude’s Connectors Directory), Supermemory https://mcp.supermemory.ai/mcp, Mem0 https://mcp.mem0.ai/mcp/, Cognee Cloud https://mcp.aws.cognee.ai/mcp.
3. In a chat, turn it on under + › Connectors, and set Tool access to Always available.
4. Tell Claude when to use it, in a Project’s instructions or by asking by name.

A connector added on claude.ai also shows up in Claude Desktop and the phone apps. Counterparts for claude.ai will work the same way, as a custom connector; it’s coming soon, in beta; sign up on the home page to hear when it opens.

Cursor and VS Code

Cursor reads .cursor/mcp.json in a project or ~/.cursor/mcp.json for every project, under mcpServers, the same shape as Desktop’s; restart Cursor after saving. In VS Code, run MCP: Add Server from the Command Palette. VS Code now labels the older .vscode/mcp.json deprecated in that flow, and its docs warn that “Adding a configuration isn’t proof that the agent can use the server”. For a brief at the start of each Cursor session, Engram Alpha registers “a sessionStart hook in .cursor/hooks.json”, and Supermemory’s Cursor plugin “Loads your persistent profile when a Cursor conversation starts”.

The reference server’s file goes missing#

The reference server is the one most guides start with, and its default file location surprises people. With no MEMORY_FILE_PATH, it writes memory.jsonl inside its own package folder. Run through npx in our throwaway test, that was npm’s cache: ~/.npm/_npx/<hash>/node_modules/@modelcontextprotocol/server-memory/dist/memory.jsonl. An open issue on the servers repo says that there “a reinstall, an update or a cleared npx cache can delete the graph”. Set MEMORY_FILE_PATH to a full path in a folder that exists, as in the examples above; a leading ~ is expanded.

In the same test, its search matches text: a note “prefers tea” came back for “tea” and not for “drinks”. And on Node 20, npx quietly ran version 1.0.0, because 2.0.0 and later need Node 24. The repo calls its servers “educational examples for developers building their own MCP servers, not as production-ready solutions”.

What people report#

These come from archived copies of r/mcp comments, read through the Arctic Shift archive; nobody opened them live on Reddit. Each scored 1 in the archive, and most of the commenters build tools of their own.

• On a shared memory across tools: “All your tools share one memory but you still have to tell them to check it.” (comment)
• On the fix that held up: “stop offering the memory as a tool and inject it instead. a hook at session start that drops a one line index of every memory into context beats hoping the model chooses to call something.” They keep the full notes behind the tool, and say the model then calls it “reliably, because it already knows the thing exists”. (comment)
• On saving, the other half: with injection, “the model cannot forget to call save, because save is not an action it controls”, at the cost that “the context window fills with junk” if the memory layer keeps the wrong things. (comment)
• On server instructions in other clients: “not every client passes it through. Cline currently drops it entirely”. (comment)
• And a case for no server at all: “put the durable project facts in a markdown file in the repo and point every agent at it from its instructions. I ran a two-agent setup like this and the file beat the memory server”. (comment)

A builder of a different kind of server saw the same thing: the model would “list the tools, and then just never call them. Nothing errored.” Rewriting the descriptions to say when to use each tool fixed it (comment).

Questions people ask#

Questions? Ask in r/AIMemorySystems.