mymemory-mcp
Click on "Deploy 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., "@mymemory-mcp fetch my memory context"
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.
mymemory-mcp
One memory for every AI. MyMemory is a user-owned, portable memory vault: import your context once (CLAUDE.md, custom instructions, bios), and every AI tool you use reads the same distilled entries. Agents propose new memories after conversations; proposals land in a pending queue you promote or reject. The memory belongs to you, not a lab. This package is the vault as an MCP server: 3 tools, zero install beyond npm.
Your context, portable. — myclawn.com/mymemory
Quick start
Get an API key first: open https://www.myclawn.com/mymemory, sign in with a
passkey, and create a key in the Keys view. Keys look like mm_… and
are shown once — the read + propose scopes cover everything below.
Claude Code
claude mcp add mymemory -e MYMEMORY_API_KEY=mm_your_key -- npx -y mymemory-mcpClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"mymemory": {
"command": "npx",
"args": ["-y", "mymemory-mcp"],
"env": {
"MYMEMORY_API_KEY": "mm_your_key"
}
}
}
}Any other MCP client: serve over stdio with command npx and args
["-y", "mymemory-mcp"]; the key goes in the MYMEMORY_API_KEY env var.
Self-hosted or testing against a local app? Point MYMEMORY_BASE_URL at
the origin (default https://www.myclawn.com).
Related MCP server: SOMA MCP
What your agent can do
Tool | What |
| Fetch the vault's compiled context block (directives first, then facts/preferences/notes) plus the raw entries — call once at conversation start |
| Case-insensitive substring search over active entries ( |
| Propose up to 20 |
The API key's scopes gate the verbs: read covers context + search,
propose covers proposing. A read-only key proposing gets a clear 403.
What it deliberately does NOT do
No approving/rejecting entries, no editing the vault, no minting or revoking keys — curation stays with the human at myclawn.com/mymemory, where every proposed entry waits in a review queue. This package is the read/propose surface: agents remember, you decide.
Links
The vault app: https://www.myclawn.com/mymemory
API + pairing surface:
https://www.myclawn.com/api/mymemory/*iOS app source (pairs via 6-char code):
ios/in the monorepo
Decisions
Search is client-side. The API contract defines no search route, so
mymemory_searchfetches/api/mymemory/contextonce and filters entries by case-insensitive substring overtext(andkind, so "directive" lists all rules). Simple and fine at vault sizes.mymemory_get_contextreturns text-first. The compiledcontext_blockleads the tool result (paste-ready), followed by the raw entries as JSON for structured use.MYMEMORY_BASE_URLis the origin, not the full API prefix; routes are appended as/api/mymemory/…. Trailing slashes are tolerated.Engines
node >=22per the build contract (siblingmyclawn-mcpsays >=20; the contract wins).Registry metadata (
server.json/smithery.yaml) ships in this package; submission steps live indocs/registry-submissions.md. The package is publish-ready (publishConfig.access: public).
MIT © MyClawn
Available Tools
3 toolsmymemory_get_contextA
Fetch the user's MyMemory vault context: a compiled context_block (directives first, then facts/preferences/notes, one per line) plus the raw active entries. Call once at conversation start and apply it — these are the user's standing rules, facts, and preferences across every AI tool they use.
Inputs: no named parameters.
mymemory_proposeA
Propose new entries to the user's MyMemory vault when you learn something durable about them. Entries land in a PENDING queue the human reviews in the app — nothing becomes active without their approval. Only propose lasting cross-session knowledge (a rule, fact, preference, or note about the user), never one-off task details or things already in the vault. kinds: directive = a rule ("never …", "always …"), fact, preference, note. Max 20 per call; the server dedupes against existing entries.
Inputs: entries (required).
mymemory_searchA
Search the user's MyMemory vault for a word or phrase. Case-insensitive substring match over active entries (text and kind). Returns { query, count, matches }.
Inputs: query (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
The three tools have distinct purposes: get_context (retrieve full vault), search (find specific entries), and propose (add new entries). While get_context and search both read data, their purposes are clearly separated (bulk fetch vs. targeted lookup), and propos e is clearly a write operation. No meaningful ambiguity between them.
All tool names follow a consistent mymemory_<verb> pattern: get_context, search, propose. The verbs are uniform and descriptive, and each name clearly signals its operation (retrieval, lookup, and write). The mymemory_ prefix consistently scopes the namespace.
Three tools is on the thin side but understandable for a personal memory vault: fetch, search, and write. The scope is narrow enough that three tools could work, though one might expect an explicit update/delete mechanism. It's at the lower boundary of reasonable.
The vault supports retrieval (get_context), search, and creation (propose, gated by PENDING approval). However, there's no tool to update or delete existing active entries, nor to approve the pending queue, meaning maintenance of the vault is delegated entirely to the human in the app. This leaves some lifecycle gaps beyond the core read/write flow.
Maintenance
Related MCP Connectors
Persistent, portable memory for AI assistants — your private memory graph, from any MCP client.
Persistent memory for AI assistants — one shared, OAuth-secured vault for every MCP client.
An MCP memory server. One memory your agents share — across models, devices and apps.
One memory, every AI. A shared, user-owned markdown memory your AI clients read and write over MCP.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceA portable MCP server providing a shared intelligent memory system for any MCP-compatible AI tool, enabling storage, retrieval, extraction, and governance of memories across sessions.16 npmMIT
- AlicenseNot gradedqualityBmaintenanceA private, self-hosted MCP server that wraps a retrieval pipeline over your own data, enabling trusted AI agents to access and manage your personal memory through standard MCP tools.Apache 2.0
- AlicenseNot gradedqualityDmaintenanceA portable self-hosted memory layer for AI tools, storing context, memories, and handoffs for access from any MCP-compatible client.48 npmMIT
- AlicenseNot gradedqualityDmaintenanceA personal memory MCP server that stores and retrieves conversation memories, enabling AI agents to recall past discussions, promises, and preferences using natural language.MIT