prometheus · MCP Server

Prometheus + Improvado MCP — Metrics, One Question Away

Improvado MCP extracts data from Prometheus and makes it queryable by any AI agent. Ask about service health, alert patterns, and metric trends without opening a single dashboard.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Ask Any Question About Your Infrastructure Metrics

Improvado MCP connects your Prometheus data to AI, so teams can query time-series metrics, alert history, and target health in plain English — no PromQL, no manual exports.

Example prompts

"Which services had highest CPU usage today?"

30 min → 20 sec

"Show all alerts fired in the last 7 days"

Manual → auto

"What's the memory trend for production pods?"

2 hrs → 1 min

Works with Claude ChatGPT Cursor +5

Write

Act on Insights Without Leaving Your AI Workflow

Update alert rules, modify recording rules, and configure targets directly from your AI agent — without switching context or writing PromQL manually.

Example prompts

"Create alert for 90% disk usage threshold"

45 min → 3 min

"Update recording rule for API latency percentiles"

Manual → auto

"Add new scrape target for staging environment"

1 hr → 2 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Stay Ahead of Performance Issues and Anomalies

Monitor metric spikes, target health changes, and alert pattern shifts automatically — your AI agent surfaces what matters before it becomes a problem.

Example prompts

"Alert if any target becomes unreachable"

Hourly manual → auto

"Which metrics show unusual spikes this week?"

Daily report → instant

"Track error rate changes across all services"

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Complex Metric Queries

The problem

Teams spend hours writing and debugging PromQL queries to extract insights from time-series data and understand system behavior.

How MCP solves it

Improvado MCP extracts Prometheus data and makes it instantly queryable via AI — no PromQL expertise needed.

Try asking

Show CPU usage trends for all pods this month

Answer in seconds

All data sources, one query

Challenge 2

Manual Alert Investigation

The problem

Investigating alert patterns, correlating incidents, and analyzing historical alert data requires navigating multiple dashboards and manual analysis.

How MCP solves it

AI agents query alert history and metric correlations directly, surfacing root causes and patterns in seconds.

Try asking

Which alerts fired most frequently last week?

Full detail preserved

No data loss on export

Challenge 3

Delayed Anomaly Detection

The problem

Unusual metric patterns, target failures, and performance degradation go unnoticed until they trigger alerts or impact users.

How MCP solves it

Continuous monitoring surfaces anomalies automatically — teams get alerts before issues escalate.

Try asking

Flag any service with response time spike above 300%

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What is Prometheus MCP?

Prometheus MCP is an integration that connects Prometheus monitoring data to AI agents via the Improvado MCP server. It allows teams to query metrics, alerts, and target health using plain-language prompts.

What data does Improvado extract from Prometheus?

Improvado extracts time-series metrics, alert history, target health status, recording rules, and scrape configuration from Prometheus, making all of it queryable through connected AI agents.

Do I need to write PromQL to use Prometheus MCP?

No. Once Improvado MCP is configured, you interact with your Prometheus data through plain-language prompts in your AI agent — no PromQL or scripting required.

Can I monitor Prometheus metrics automatically?

Yes. You can set up AI-driven monitoring that tracks metric trends, alert patterns, and target health — surfacing anomalies without manual dashboard reviews.

How is this different from Grafana or the Prometheus UI?

Dashboards require manual navigation and pre-built queries. Improvado MCP makes the same data available to AI agents that can query, correlate, and act on it alongside data from other tools.

Which AI agents work with Prometheus MCP?

Improvado MCP works with any MCP-compatible AI agent, including Claude, custom LLM pipelines, and enterprise AI platforms that support the Model Context Protocol.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms