recall-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@recall-mcpsearch for deployment preferences"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
recall-mcp
Persistent, searchable memory for AI agents — over the Model Context Protocol.
Give any MCP-capable agent (Claude Code, Claude Desktop, Cursor, Windsurf, ...) a long-term memory it can write to and search across sessions. Facts, preferences, decisions, and lessons survive restarts and are retrieved by relevance-ranked full-text search.
🧠 Six tools: save, search, recent, get, forget, stats
🔎 Real search: SQLite FTS5 with BM25 ranking, porter stemming, and an importance boost — not a
LIKE '%...%'scan📦 Zero native dependencies: uses Node's built-in
node:sqlite(Node ≥ 23.4). No compilers, no prebuilt binaries, nothing to break on install🗃️ Single-file storage: one SQLite database at
~/.recall-mcp/memories.db(WAL mode), easy to back up or inspect
Quick start
git clone https://github.com/jadeleke/recall-mcp.git
cd recall-mcp
npm install
npm run buildClaude Code
claude mcp add recall -- node /path/to/recall-mcp/dist/index.jsClaude Desktop / other MCP clients
Add to your MCP config (e.g. claude_desktop_config.json):
{
"mcpServers": {
"recall": {
"command": "node",
"args": ["/path/to/recall-mcp/dist/index.js"]
}
}
}Related MCP server: MemoryClaw
Tools
Tool | What it does |
| Persist a fact with optional |
| Full-text search, BM25-ranked with importance boost. FTS5 syntax (phrases, |
| Most recently saved/updated memories, newest first |
| Fetch one memory by id |
| Permanently delete a memory by id |
| Totals, tag histogram, date range, db location and size |
Example
An agent mid-conversation:
memory_save({
content: "User deploys to Fly.io from CI only — never deploy from a laptop",
tags: ["ops", "user-preference"],
importance: 5
})Weeks later, in a fresh session:
memory_search({ query: "how does the user deploy" })returns the fact, ranked first thanks to porter stemming (deploy matches deploys) and the importance boost.
Configuration
Env var | Default | Purpose |
|
| Database file path. Set per-project paths to keep separate memory stores; |
Development
npm test # node:test suite — runs TypeScript directly via Node's type stripping
npm run build # emits dist/The codebase is small on purpose: src/store.ts is the storage layer (schema, FTS5 triggers, search), src/index.ts wires it to MCP over stdio.
Why not just a system prompt?
System prompts are per-session and hand-curated. recall-mcp lets the agent decide what's worth keeping, accumulates knowledge across every session and every MCP client that shares the database, and retrieves only what's relevant to the current task instead of stuffing everything into context.
License
MIT
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