CrewAIFramework

Prometheus MCP Server

Bring Prometheus
to CrewAI

Name: Prometheus MCP Server
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

Learn how to connect Prometheus to CrewAI and start using 14 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Clean TombstonesCreate SnapshotDelete SeriesFind SeriesGet Label ValuesGet LabelsGet MetadataGet Status BuildinfoGet Status ConfigGet Status FlagsGet Status RuntimeinfoGet Status TsdbQueryQuery Range

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the Prometheus MCP Server?

Connect your Prometheus instance to any AI agent and transform your observability data into actionable insights through natural conversation.

What you can do

Instant & Range Queries — Evaluate complex PromQL expressions for real-time status or historical trends over specific time windows.
Metric Discovery — Find time series matching specific label selectors and explore available labels and their values across your environment.
Metadata Inspection — Retrieve detailed metadata about metrics scraped from targets to understand units, types, and help text.
Admin Operations — Create data snapshots, delete specific series, and clean tombstones (requires admin API enabled).
System Status — Inspect your Prometheus configuration, flags, and runtime information to ensure your monitoring stack is healthy.

How it works

Subscribe to this server
Enter your Prometheus Server URL (and optional Auth Token)
Start querying your metrics from Claude, Cursor, or any MCP-compatible client

No more manual dashboard building just to answer a quick question about system health. Your AI acts as a dedicated SRE or DevOps engineer.

Who is this for?

SRE & DevOps Engineers — instantly troubleshoot incidents by querying metrics and checking configurations without leaving the terminal or chat.
Backend Developers — verify service performance and resource consumption directly from the code editor.
Platform Teams — automate infrastructure health reports and audit monitoring configurations via natural language.

Built-in capabilities (14)

clean_tombstones

enable-admin-api to be enabled. Remove deleted data from disk

create_snapshot

enable-admin-api to be enabled on the Prometheus server. Create a snapshot of all current data

delete_series

enable-admin-api to be enabled. Delete data for a selection of series in a time range

find_series

Find time series matching label selectors

get_label_values

Get all values for a specific label

get_labels

Get a list of all label names

get_metadata

Get metadata about metrics scraped from targets

get_status_buildinfo

Get Prometheus build information

get_status_config

Get the currently loaded Prometheus configuration (YAML)

get_status_flags

Get configured Prometheus flag values

get_status_runtimeinfo

Get Prometheus runtime information

get_status_tsdb

Get TSDB cardinality statistics

query

Evaluate a PromQL expression at a single point in time

query_range

Evaluate a PromQL expression over a range of time

Why CrewAI?

When paired with CrewAI, Prometheus becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Prometheus tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

—
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
—
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the mcps parameter and agents auto-discover every available tool at runtime
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Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
—
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

See it in action

Prometheus in CrewAI

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Prometheus and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Prometheus to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	Raw MCP	Vinkius
Server catalog	Find and host yourself	4,000+ managed
Infrastructure	Self-hosted	Sandboxed V8 isolates
Credential handling	Plaintext in config	Vault + runtime injection
Data loss prevention	None	Configurable DLP policies
Kill switch	None	Global instant shutdown
Financial circuit breakers	None	Per-server limits + alerts
Audit trail	None	Ed25519 signed logs
SIEM log streaming	None	Splunk, Datadog, Webhook
Honeytokens	None	Canary alerts on leak
Custom domains	Not applicable	DNS challenge verified
GDPR compliance	Manual effort	Automated purge + export

Unlock for AI Agents View step-by-step setup guide for CrewAI →

Enterprise Security

Why teams choose Vinkius for Prometheus in CrewAI

The Prometheus MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 14 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Unlock for AI Agents View full Prometheus details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

The Vinkius Advantage

How Vinkius secures
Prometheus for CrewAI

Every tool call from CrewAI to the Prometheus MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

Can I run a PromQL query to get the current value of a metric?

Yes. Use the query tool to evaluate any PromQL expression at a single point in time. This is perfect for checking current CPU usage, memory levels, or error rates.

How do I see how a metric has changed over the last hour?

Use the query_range tool. You can specify the start and end timestamps along with a step duration to retrieve historical data points for graphing or trend analysis.

Can I perform administrative tasks like creating backups?

Yes, if your Prometheus server has the Admin API enabled (--web.enable-admin-api), you can use the create_snapshot tool to create a snapshot of all current data on disk.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

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