Monitoring & Observability Basic Auth

Prometheus REST API

Pull-based monitoring system with powerful query language

Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It collects and stores metrics as time series data, providing a powerful query language (PromQL) for analysis and visualization. Developers use Prometheus to monitor infrastructure, applications, and services with dimensional data modeling and built-in alerting.

Base URL http://localhost:9090/api/v1

API Endpoints

Method	Endpoint	Description
GET	`/query`	Evaluates an instant query at a single point in time
GET	`/query_range`	Evaluates an expression query over a range of time
GET	`/series`	Returns the list of time series that match a certain label set
GET	`/labels`	Returns a list of label names
GET	`/label//values`	Returns a list of label values for a provided label name
GET	`/targets`	Returns an overview of the current state of the Prometheus target discovery
GET	`/rules`	Returns a list of alerting and recording rules
GET	`/alerts`	Returns a list of all active alerts
GET	`/alertmanagers`	Returns an overview of the current state of the Prometheus alertmanager discovery
GET	`/status/config`	Returns the current Prometheus configuration
GET	`/status/flags`	Returns the flag values that Prometheus was configured with
GET	`/status/runtimeinfo`	Returns runtime information about the Prometheus server
GET	`/metadata`	Returns metadata about metrics currently scraped from targets
POST	`/admin/tsdb/snapshot`	Creates a snapshot of all current data into a subdirectory
POST	`/admin/tsdb/delete_series`	Deletes data for a selection of series in a time range

Code Examples

curl -G 'http://localhost:9090/api/v1/query' \
  --data-urlencode 'query=up' \
  --data-urlencode 'time=2024-01-15T10:30:00.000Z' \
  -u 'admin:password'

const queryMetrics = async () => {
  const params = new URLSearchParams({
    query: 'rate(http_requests_total[5m])',
    time: new Date().toISOString()
  });
  
  const response = await fetch(
    `http://localhost:9090/api/v1/query?${params}`,
    {
      headers: {
        'Authorization': 'Basic ' + btoa('admin:password')
      }
    }
  );
  
  const data = await response.json();
  console.log(data.data.result);
};

queryMetrics();

import requests
from requests.auth import HTTPBasicAuth
from datetime import datetime

url = 'http://localhost:9090/api/v1/query'
params = {
    'query': 'up{job="prometheus"}',
    'time': datetime.utcnow().isoformat() + 'Z'
}

response = requests.get(
    url,
    params=params,
    auth=HTTPBasicAuth('admin', 'password')
)

data = response.json()
for result in data['data']['result']:
    print(f"Metric: {result['metric']}, Value: {result['value'][1]}")

Connect Prometheus to AI

Deploy a Prometheus MCP server on IOX Cloud and connect it to Claude, ChatGPT, Cursor, or any AI client. Your AI assistant gets direct access to Prometheus through these tools:

query_prometheus_metrics Execute PromQL queries to retrieve current metric values and analyze system health

get_metric_history Query time-series data over a range to analyze trends and patterns in metrics

list_active_alerts Retrieve all currently firing alerts with their labels and annotations

discover_targets Get the current status of all monitored targets and their health state

analyze_metric_cardinality Examine metric labels and cardinality to optimize monitoring performance

Deploy in 60 seconds

Describe what you need, AI generates the code, and IOX deploys it globally.

Deploy Prometheus MCP Server →

Prometheus REST API

API Endpoints

Code Examples

Connect Prometheus to AI

Deploy in 60 seconds

Related APIs