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., "@ContextVault store my current task: working on login"
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.
Shared context storage for AI agents. Any MCP-compatible model (Claude, ChatGPT, Gemini) can store, read, and search context entries through the MCP server. A web UI lets you browse and manage everything.
Claude / ChatGPT / Gemini
│ (MCP stdio)
▼
┌──────────────┐
│ MCP Server │──────┐
└──────────────┘ │
▼
┌──────────────┐ ┌──────────┐
│ Web UI │──▶│ Express │──▶ PostgreSQL
└──────────────┘ └──────────┘Express API (src/index.ts) — REST API + serves the React UI
MCP Server (src/mcp.ts) — stdio MCP server for AI agents
PostgreSQL — persistent storage with JSONB tags and GIN index
Related MCP server: Memclaw
docker compose upOpen http://localhost:3000 for the UI. Postgres runs on port 5432.
Prerequisites: Node.js 20+, PostgreSQL running locally.
# Create the database
createdb context_vault
# Install and build
npm install
cd client && npm install && npm run build && cd ..
npm run build:server
# Start the web server
npm start
# Or run the MCP server for AI agents
npm run mcpSet DATABASE_URL env var if your Postgres isn't at the default postgresql://postgres:postgres@localhost:5432/context_vault.
{
"mcpServers": {
"context-vault": {
"command": "node",
"args": ["/absolute/path/to/context-vault/dist/mcp.js"],
"env": {
"DATABASE_URL": "postgresql://postgres:postgres@localhost:5432/context_vault"
}
}
}
}# Build and push
gcloud builds submit --tag gcr.io/PROJECT_ID/context-vault
# Deploy
gcloud run deploy context-vault \
--image gcr.io/PROJECT_ID/context-vault \
--set-env-vars DATABASE_URL=postgresql://... \
--port 8080 \
--allow-unauthenticatedMethod | Path | Description |
GET |
| Dashboard stats |
GET |
| List namespaces |
GET |
| List entries (query: |
GET |
| Search entries |
GET |
| Get one entry |
POST |
| Create/upsert entry |
PUT |
| Update entry |
DELETE |
| Delete entry |
Tool | Description |
| Store or upsert a context entry |
| Retrieve a specific entry |
| Partially update an entry |
| Delete an entry |
| List entries with filters |
| Search by keyword |
| List all namespaces |
This server cannot be deployed
shared AI-context layer for teams — persistent memory your agents search and update over MCP
Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.
Persistent memory for AI agents — log and recall conversation context over MCP.
Cross-tool persistent memory and context for AI assistants over MCP.
