# RCLL MCP server

Self-hosted shared memory for a team of AI agents. Rooms, L0-L3 depth, no LLM on the read path.

## At a glance

RCLL is an MCP server in the AI & Memory category. It runs locally over stdio via `npx -y fleet-memory-mcp`, and declares no required credentials. It exposes 5 tools, including memory_retain, memory_recall and memory_reflect. It scores A (83/100) on the MCP Skills Hub health score as of Oct 28, 2026.

## Install

### How to install RCLL in Claude Code

1. Add the server

```bash
claude mcp add fleet-memory -- npx -y fleet-memory-mcp
```

2. Verify it's connected

```bash
claude mcp list
```

3. Sharing it with your team?

   Add `--scope project` to write it to .mcp.json in the repository root, which you can commit (with the secrets left as environment variables).

4. Use it in a session

   Ask Claude to use the tools this server exposes (e.g. memory_retain, memory_recall, memory_reflect). It discovers them automatically.

### How to install RCLL in Claude Desktop

1. Open your MCP config

   macOS: ~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

2. Add this server

```json
{
  "mcpServers": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}
```

3. Restart Claude Desktop

   The tools appear under the 🔌 menu once the app reloads.

### How to install RCLL in Cursor

1. Create / edit .cursor/mcp.json in your project

```json
{
  "mcpServers": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}
```

2. Enable it

   Cursor Settings → MCP → toggle the server on. Tools become available to the Composer/Agent.

### How to install RCLL in Codex CLI

1. Add to ~/.codex/config.toml

```toml
[mcp_servers.fleet-memory]
command = "npx"
args = ["-y", "fleet-memory-mcp"]
```

2. Run Codex

   Codex loads MCP servers from config.toml on startup and exposes their tools to the agent.

### How to install RCLL in Windsurf

1. Edit ~/.codeium/windsurf/mcp_config.json

```json
{
  "mcpServers": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}
```

2. Reload

   Windsurf → Cascade → refresh MCP servers. Cascade can now call the tools.

### How to install RCLL in Cline

1. Open Cline → MCP Servers → Configure

   Adds to cline_mcp_settings.json.

2. Add the server

```json
{
  "mcpServers": {
    "fleet-memory": {
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}
```

### How to install RCLL in VS Code

1. Create .vscode/mcp.json

```json
{
  "servers": {
    "fleet-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "fleet-memory-mcp"
      ]
    }
  }
}
```

2. Start it

   VS Code shows a ▶ Start action above the server entry. Agent mode (Copilot Chat) can then use the tools.

## Tools

- `memory_retain` — Save a memory with automatic room/hall classification
- `memory_recall` — Scoped semantic search with room/hall/layer filters
- `memory_reflect` — Deep reasoning — synthesize facts, find patterns, answer with citations
- `memory_compress` — Create closet summaries from accumulated facts
- `memory_bridge` — Cross-bank tunnels between related memories

Source repository: https://github.com/Holetron-lab/fleet-memory

---

Source: https://mcp-skills-hub.vercel.app/mcps/ai-rcll-fleet-memory · MCP Skills Hub · data as of 2026-10-28T12:31:05Z