Which integrations are available for this server?

Deploys as a Cloudflare Worker with D1 database backend, providing MCP protocol access and REST API endpoints for managing a knowledge graph with entities, observations, and relations. Provides workflow integration for querying the memory API, supporting search, entity retrieval, and graph operations through HTTP Request nodes with webhook and manual triggers.

How do I use Memory MCP Worker?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Memory MCP Worker search for my project notes about the budget planning tool" 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.

Memory MCP Worker

by 45black-Limited

Overview Schema Source Related Servers Discussions Admin

TypeScript

Remote

Memory MCP Worker

Cloudflare Worker implementing MCP protocol for cross-device memory/knowledge graph access.

Features

MCP Protocol Support: Works with Claude Code via HTTP transport
REST API: Access from mobile apps, desktop apps, n8n workflows
D1 Database: SQLite-based persistent storage
Full-text Search: Search across all observations

Related MCP server: mcp-brain

Quick Start

1. Install Dependencies

npm install

2. Create D1 Database

# Create the database
wrangler d1 create memory-graph

# Copy the database_id from output to wrangler.toml

3. Run Migrations

# Local development
npm run db:migrate:local

# Production
npm run db:migrate

4. Deploy

npm run deploy

5. Set API Key Secret

# Generate an API key
openssl rand -base64 32 | tr -d '/+=' | head -c 32

# Add as secret
echo "YOUR_API_KEY" | npx wrangler secret put API_KEY

6. Configure Claude Code

Add to your ~/.claude/settings.json or .mcp.json:

{
  "mcpServers": {
    "memory-remote": {
      "type": "http",
      "url": "https://memory-mcp.45black-limited.workers.dev/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

Migrating from Local Memory

Export your local memory:

claude -p "Use mcp__memory__read_graph and output the raw JSON result" > scripts/local-memory.json

Run migration:

npx ts-node scripts/migrate-local-memory.ts

API Endpoints

REST API (for apps)

Endpoint	Method	Description
`/api/entities`	GET	List all entities
`/api/entities/:name`	GET	Get entity with observations
`/api/entities`	POST	Create entity
`/api/entities/:name/observations`	POST	Add observations
`/api/relations`	GET	List all relations
`/api/relations`	POST	Create relation
`/api/search?q=`	GET	Search entities and observations
`/api/graph`	GET	Get full graph
`/api/import`	POST	Bulk import

MCP Protocol

Endpoint	Method	Description
`/mcp`	POST	MCP JSON-RPC requests
`/mcp/sse`	GET	SSE connection for MCP

MCP Tools Available

create_entities - Create new entities
create_relations - Create relations between entities
add_observations - Add observations to entities
read_graph - Read entire knowledge graph
search_nodes - Search by query
open_nodes - Get specific nodes by name
delete_entities - Delete entities
delete_relations - Delete relations

Authentication

All /api/* and /mcp/* endpoints require authentication via API key.

Headers supported:

X-API-Key: YOUR_API_KEY
Authorization: Bearer YOUR_API_KEY

The health check endpoint (/) is public.

Mobile App Integration

Example fetch from a mobile app:

const API_KEY = 'YOUR_API_KEY';
const BASE_URL = 'https://memory-mcp.45black-limited.workers.dev';

const headers = {
  'Content-Type': 'application/json',
  'X-API-Key': API_KEY,
};

// Search for a project
const response = await fetch(`${BASE_URL}/api/search?q=Household%20Planner`, { headers });
const data = await response.json();

// Add an observation
await fetch(`${BASE_URL}/api/entities/Household%20Planner/observations`, {
  method: 'POST',
  headers,
  body: JSON.stringify({
    contents: ['Added new budget category feature']
  })
});

n8n Integration

An n8n workflow is included for querying the Memory API via webhooks.

Setup

Import n8n-workflow-memory-api.json into n8n
Create a "Header Auth" credential:
- Name: Memory MCP API Key
- Header Name: X-API-Key
- Header Value: YOUR_API_KEY
Assign the credential to all three HTTP Request nodes
Activate the workflow

Endpoints

Search - POST to /webhook/memory-search:

curl -X POST https://your-n8n-instance/webhook/memory-search \
  -H "Content-Type: application/json" \
  -d '{"query": "Household Planner"}'

Get Entity - POST to /webhook/memory-entity:

curl -X POST https://your-n8n-instance/webhook/memory-entity \
  -H "Content-Type: application/json" \
  -d '{"name": "Household Planner"}'

Get Full Graph - POST to /webhook/memory-graph:

curl -X POST https://your-n8n-instance/webhook/memory-graph \
  -H "Content-Type: application/json" \
  -d '{}'

Create Entity - POST to /webhook/memory-create:

curl -X POST https://your-n8n-instance/webhook/memory-create \
  -H "Content-Type: application/json" \
  -d '{"name": "My Project", "entityType": "Project", "observations": ["First observation"]}'

Add Observations - POST to /webhook/memory-observations:

curl -X POST https://your-n8n-instance/webhook/memory-observations \
  -H "Content-Type: application/json" \
  -d '{"name": "My Project", "contents": ["New observation 1", "New observation 2"]}'

Create Relation - POST to /webhook/memory-relations:

curl -X POST https://your-n8n-instance/webhook/memory-relations \
  -H "Content-Type: application/json" \
  -d '{"from": "Project A", "to": "Project B", "relationType": "depends_on"}'

Delete Entity - POST to /webhook/memory-delete:

curl -X POST https://your-n8n-instance/webhook/memory-delete \
  -H "Content-Type: application/json" \
  -d '{"name": "Entity Name"}'

Delete Relation - POST to /webhook/memory-delete-relation:

curl -X POST https://your-n8n-instance/webhook/memory-delete-relation \
  -H "Content-Type: application/json" \
  -d '{"from": "Entity A", "to": "Entity B", "relationType": "relation_type"}'

Development

# Start local dev server
npm run dev

# Typecheck
npm run typecheck

Architecture

┌─────────────────────────────────────────┐
│         Cloudflare Worker               │
│      memory-mcp.45black.workers.dev     │
├─────────────────────────────────────────┤
│  Hono Framework                         │
│  ├── /api/* → REST API                  │
│  └── /mcp   → MCP Protocol              │
├─────────────────────────────────────────┤
│  Cloudflare D1 (SQLite)                 │
│  ├── entities                           │
│  ├── observations                       │
│  ├── relations                          │
│  └── observations_fts (full-text)       │
└─────────────────────────────────────────┘

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Related MCP Connectors

BrainboxOAuth
dev.thexi
Persistent, portable memory for AI assistants — your private memory graph, from any MCP client.
Knowledge & Memory AI & Machine Learning
2
Mnema — AI Memory Layer
io.github.hoanglt1223
Personal knowledge graph as an AI memory layer over MCP - read, save, and link your memories.
Knowledge & Memory AI & Machine Learning
2
MemoryRouter A
ai.memoryrouter
Persistent AI memory shared across Claude, ChatGPT, coding agents, and compatible MCP clients.
Knowledge & Memory Search
101
memnodeOAuth
dev.memnode
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Knowledge & Memory Autonomous Agents Agent Orchestration
2

Related MCP Servers

second-brain-cloudflare
Knowledge & Memory Vector Databases Search
rahilp
A
license
Not graded
quality
A
maintenance
Self-hosted semantic memory layer for Claude and MCP-compatible AI clients. Store notes, search by meaning not keywords, and recall relevant context automatically across sessions. Runs free on Cloudflare Workers, D1, Vectorize, and Workers AI
Updated 2 days ago (2026-09-14 02:42 UTC)
3 npm
764
MIT
mcp-brain
Knowledge & Memory Search RAG Systems
dabielf
A
license
Not graded
quality
D
maintenance
A personal second brain on Cloudflare Workers that stores knowledge as a semantic graph with auto-linking, deduplication, and MCP tools for AI agents to save, search, and connect information.
Updated 6 months ago (2026-03-16 01:37 UTC)
21 npm
2
MIT
SkyBrain
Note Taking Knowledge & Memory
augwalts
A
license
Not graded
quality
C
maintenance
Provides a shared MCP memory layer for AI clients, backed by Cloudflare Workers and D1, enabling personal Markdown notes management.
Updated 3 months ago (2026-06-25 19:49 UTC)
MIT
Mnemosyne
Knowledge & Memory Search
M4F-S
A
license
A
quality
A
maintenance
Provides persistent, graph-based memory for AI agents via MCP, enabling semantic search, wikilink traversal, reminders, and injection protection.
Updated 14 days ago (2026-09-01 18:46 UTC)
9
30
Apache 2.0

Memory MCP Worker

Features

Quick Start

1. Install Dependencies

2. Create D1 Database

3. Run Migrations

4. Deploy

5. Set API Key Secret

6. Configure Claude Code

Migrating from Local Memory

API Endpoints

REST API (for apps)

MCP Protocol

MCP Tools Available

Authentication

Mobile App Integration

n8n Integration

Setup

Endpoints

Development

Architecture

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

second-brain-cloudflare

mcp-brain

SkyBrain

Mnemosyne