Mem MCP

Live

OAUTH 2.0

AI

AI

A hosted MCP server that gives AI tools secure access to your Mem notes and collections — enabling AI agents to read, create, search, and organize notes...

  • Acts as the user: Every tool call runs as the authorizing user. Access and audit trail stay intact.
  • Credentials stay vaulted: AES-256 encrypted, resolved at request time, never stored in LLM context.
  • Scoped before every call: Per-user permissions enforced automatically. 90-day audit trail included.
Mem MCP
agent · Acme Q3
Run
Add Note To Collection in Mem MCP
S
memmcp_add_note_to_collection
85ms
Mem MCP agent
Add an existing note to an existing collection. this operation only creates the membership link and does not modify note.
Sources: Mem MCP
memmcpmcp
1 tool call
18:29
Message Claude...

Tools your agent reaches for on Mem, scoped per user.

CALL ANY TOOL
Create, search, and organize notes and collections in the user's own Mem workspace.
memmcp_list_notes
List notes
List notes visible to the authenticated caller with cursor pagination. When multiple `contains_*` fields are true, a note may match any of them. Results are ordered by `order_by` and return `next_page` when additional rows are available. For relevance-ranked retrieval by query, use `search_notes`. When to use: - You need deterministic cursor pagination ordered by `updated_at` or `created_at`. - You are iterating through all accessible notes page by page. When NOT to use: - You need relevance-ranked retrieval from open-ended text (`search_notes`).
Parameters
Name
Type
Required
Description
collection_id
string
Optional
Optional collection filter by UUID. When set, only notes linked to this collection are returned.
contains_files
boolean
Optional
When true, include notes that contain file-like attachments (including file and PDF kinds). When multiple `contains_*` fields are true, notes matching any selected filter may be returned.
contains_images
boolean
Optional
When true, include notes that contain image media (including image and GIF kinds). When multiple `contains_*` fields are true, notes matching any selected filter may be returned.
contains_open_tasks
boolean
Optional
When true, include notes that contain at least one open task item. When multiple `contains_*` fields are true, notes matching any selected filter may be returned.
contains_tasks
boolean
Optional
When true, include notes that contain at least one task item (open or closed). When multiple `contains_*` fields are true, notes matching any selected filter may be returned.
filter_by_created_after
string
Optional
Optional inclusive lower bound for note creation time (ISO 8601). The timestamp must include a timezone offset such as `Z` or `+01:00`.
filter_by_created_before
string
Optional
Optional inclusive upper bound for note creation time (ISO 8601). The timestamp must include a timezone offset such as `Z` or `+01:00`.
filter_by_updated_after
string
Optional
Optional inclusive lower bound for note update time (ISO 8601). The timestamp must include a timezone offset such as `Z` or `+01:00`.
filter_by_updated_before
string
Optional
Optional inclusive upper bound for note update time (ISO 8601). The timestamp must include a timezone offset such as `Z` or `+01:00`.
include_note_content
boolean
Optional
When true, include full markdown content for each returned note. IMPORTANT: This increases payload size and can increase latency.
limit
integer
Optional
Maximum number of notes in this page. Use smaller values for lower latency. Default is 50; valid range is 1 to 100.
order_by
string
Optional
Sort key for pagination boundaries. Use `updated_at` (default) for recency feeds. Use `created_at` for creation-order views.
page
string
Optional
Opaque cursor from a previous list response. Omit for the first page. IMPORTANT: Reuse with the same filters and `order_by` settings.
memmcp_search_notes
Search notes
memmcp_list_collections
List collections
memmcp_search_collections
Search collections
memmcp_get_note
Get note
memmcp_create_note
Create note
memmcp_get_collection
Get collection
memmcp_update_note
Update note
memmcp_get_audio_recording
Get audio recording
memmcp_create_collection
Create collection
memmcp_get_note_attachment_download_url
Get note attachment download url
memmcp_update_collection
Update collection
memmcp_add_note_to_collection
Add note to collection
memmcp_move_note
Move note
memmcp_trash_note
Trash note
memmcp_restore_note
Restore note
memmcp_read_attachment
Read attachment
memmcp_find_related_notes
Find related notes
memmcp_extended_search_notes
Extended search notes
memmcp_set_note_created_at
Set note created at
memmcp_answer_question_about_attachment
Answer question about attachment
memmcp_delete_collection
Delete collection
memmcp_remove_note_from_collection
Remove note from collection
Build your Agent
Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.
Python · LlamaIndex
from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="memmcp")

mcp = MultiServerMCPClient({
"memmcp": {
"url": "https://mcp.scalekit.com/memmcp",
"headers": {"Authorization": "Bearer " + token}
}
})
tools = await mcp.get_tools()
import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "memmcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/memmcp
// Pass: Authorization: Bearer + token
import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "memmcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/memmcp
// Pass: Authorization: Bearer + token
from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="memmcp")
# Connect to MCP at https://mcp.scalekit.com/memmcp
# Pass: Authorization: Bearer + token
Try these prompts
Copy any prompt into your agent. Each maps directly to a Mem tool. Click to copy, paste into your agent, done.
Get started
Copy the prompt
Copied
Submit a complete markdown body for a note and the exact version being updated?
Copy the prompt
Copied
Soft-delete a note by moving it to trash?
Advanced
Copy the prompt
Copied
Set a note’s visible creation timestamp without changing its content?
Copy the prompt
Copied
Search notes using a required free-text query and structured filters?
SEE HOW AUTH WORKS
Your users connect once. Their Mem credentials stay vaulted, every call is scope-checked, and every action is logged.
1
Authorize
Your user connects
Mem MCP
once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access
Who:
user ‘A’
when:
Once per user
access:
Limited to user
2
Store
Their
Mem MCP
token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection
vault:
encrypted
scope:
per-user
tokens:
auto-refreshed
3
Resolve
When your agent calls a
Mem MCP
tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs
speed:
~40ms
check:
before every call
seen by:
nobody
4
Audit
Every
Mem MCP
tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it
history:
90 days
export:
SIEM-ready
logged:
every call
Test other agents
See the same per-user auth pattern across other connectors.
GTM and RevOps Teams
Competitive intelligence briefing agent
Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.
GTM and RevOps Teams
Outbound prospecting agent
Searches Apollo for prospects matching your ICP, scores and ranks them, drafts personalized outreach in Gmail, and logs every send to Google Sheets. Mail goes out as the rep, not from a shared inbox.
GTM and RevOps Teams
Deal intelligence agent
Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.
Support and Ops Teams
Support triage agent
Fetches new Zendesk tickets, classifies them by type and urgency, searches the Notion knowledge base for an answer, and routes what it cannot resolve to Slack. Every call runs on the support agent's own delegated OAuth.
Test other agents
See the same per-user auth pattern across other connectors.
GTM
Competitive intelligence briefing agent
Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.
SALES
Outbound prospecting agent
Search Apollo for ICP matches, rank them, draft personalised Gmail outreach, and log every send to Google Sheets.
SALES
Deal intelligence agent
Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.
SUPPORT
Support triage agent
Classify new Zendesk tickets, search the Notion knowledge base for an answer, and route what it cannot resolve to Slack.
Why Scalekit
Secure your agent's access. Connectors ship in minutes
01.
Shared tokens break per-user analytics
A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential.
// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓
02.
Authentication is not authorization
03.
Multi-tenancy is architectural
04.
One connector today. Ten tomorrow.
“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”
Venu Madhav Kattagoni
Head of Engineering / Von
FAQs
Frequently Asked Questions

Does the agent access Mem as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Mem OAuth token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Mem?
Yes. Pass a tool name filter to listScopedTools so the AI agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Mem.

What happens when a user revokes Mem access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Can the agent read all my Mem notes?
Only the authorizing user's own notes and collections. Search, creation, and organization run per user, so personal knowledge bases stay personal.

Start in your coding agent
Up and running in one command
Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up
Claude Code REPL
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""memmcp"": {
""url"": ""https://mcp.scalekit.com/memmcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.memmcp]
url = ""https://mcp.scalekit.com/memmcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""memmcp"": {
""url"": ""https://mcp.scalekit.com/memmcp"",
""type"": ""http""
}
}
}