justinx

Use justinx for real-time streaming data -- MQTT brokers, Kafka topics, webhooks -- piped directly into your AI agent via MCP. Connect a data source, read live messages, set up automated alerts and anomaly detection, and get WebSocket URLs to embed in generated apps.

When to use this skill

• You need to connect to an MQTT broker (IoT sensors, industrial telemetry, smart devices)
• You need to consume from Kafka topics
• You need a webhook endpoint to receive pushed data
• You want to build a live dashboard on streaming data
• You need automated alerting or anomaly detection on a data stream
• You want a WebSocket URL that any frontend can subscribe to for real-time updates

Setup

1. Get an API key

Sign up at https://justinx.ai and copy your API key from Dashboard > Settings.

2. Configure the MCP server

Add JustinX as an MCP server. Choose one of the following methods depending on your environment.

Direct MCP config (Claude Code, Cursor, or any MCP client):

Add to your MCP settings (e.g. .claude/settings.json, ~/.openclaw/openclaw.json, or your tool's MCP config):

{
  "mcpServers": {
    "justinx": {
      "url": "https://api.justinx.ai/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

Via mcporter (if you have the mcporter skill installed):

mcporter add justinx --url https://api.justinx.ai/mcp --header "Authorization: Bearer YOUR_API_KEY"

Then call tools with:

mcporter call justinx.list_connections
mcporter call justinx.create_connection type=mqtt broker=broker.emqx.io topics='["sensors/#"]'

Tools reference

Common workflows

Connect to an MQTT broker and read data

# Connect to a public IoT demo broker
create_connection type=mqtt broker=broker.emqx.io port=8883 tls=true topics=["justinx/demo/#"]

# Read the last 5 minutes of data + 3 seconds of live entries
read_stream connectionId=\x3Cid> backfillSeconds=300 liveSeconds=3 maxEntries=50

For a private broker with credentials:

create_connection type=mqtt broker=my-broker.example.com port=8883 tls=true username=myuser password=mypass topics=["sensors/#","alerts/#"]

Create a webhook endpoint

# Creates an HTTP ingest URL -- POST JSON to it and messages appear in the stream
create_connection type=webhook

# The response includes an ingestUrl. Send data to it:
# POST https://api.justinx.ai/connections/\x3Cid>/ingest

Connect to Kafka

create_connection type=kafka brokers=["kafka1.example.com:9092"] kafkaTopics=["events","logs"]

# With SASL auth:
create_connection type=kafka brokers=["kafka.example.com:9092"] kafkaTopics=["events"] saslUsername=user saslPassword=pass ssl=true

Create a watcher for alerts

Watchers are managed automations that continuously monitor a connection for conditions you define — threshold alerts, metric aggregation, or notifications. Each watcher is scoped to a single connection.

# Create a watcher that alerts when temperature exceeds a threshold
create_watcher connectionId=\x3Cid> config='{"threshold": 45}'

# The platform provides a script template. See https://justinx.ai/docs for
# watcher script examples and the full scripting reference.

Manage watchers

# List all watchers on a connection
list_watchers connectionId=\x3Cid>

# Check logs for debugging
get_watcher_logs connectionId=\x3Cid> watcherId=\x3Cwid>

# Update threshold without redeploying
update_watcher_config connectionId=\x3Cid> watcherId=\x3Cwid> config='{"threshold": 50}'

# Restart a crashed watcher
restart_watcher connectionId=\x3Cid> watcherId=\x3Cwid>

# Remove a watcher
delete_watcher connectionId=\x3Cid> watcherId=\x3Cwid>

Build a live dashboard

After creating a connection, use the WebSocket URL from the response to build a frontend:

1. Call create_connection or list_connections to get the WebSocket URL
2. The WebSocket sends a backfill message on connect (recent history), then individual entry messages in real time
3. Each entry has { id, fields: { topic, payload }, ts } format
4. Pass the WebSocket URL to any generated React/Next.js/HTML app

WebSocket message format:

// Backfill (sent once on connect)
{ "type": "backfill", "entries": [{ "id": "...", "fields": { "topic": "...", "payload": "..." }, "ts": 1234567890 }] }

// Live entry (streamed continuously)
{ "type": "entry", "id": "...", "fields": { "topic": "...", "payload": "..." }, "ts": 1234567890 }

Topic filtering: append ?topics=sensor/temp,sensor/humidity to the WebSocket URL.

Tips

• Every new account gets a demo connection to broker.emqx.io with live IoT data -- call list_connections to find it
• Use read_stream with backfillSeconds=0 liveSeconds=5 to see only fresh data
• Watcher config is passed as a JSON string and can be updated without redeploying
• Watcher alerts appear on the connection's WebSocket stream automatically
• The WebSocket URL works from any client (browser, Node.js, Python, mobile) -- no SDK needed
• Full tool reference and parameter schemas: https://justinx.ai/llms-full.txt