Prometheus MCP Server for CrewAIGive CrewAI instant access to 14 tools to Clean Tombstones, Create Snapshot, Delete Series, and more

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

GDPR Free for Subscribers

Connect your CrewAI agents to Prometheus through Vinkius, pass the Edge URL in the `mcps` parameter and every Prometheus tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The Prometheus MCP Server for CrewAI is a standout in the Loved By Devs category — giving your AI agent 14 tools to work with, ready to go from day one.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Prometheus Specialist",
    goal="Help users interact with Prometheus effectively",
    backstory=(
        "You are an expert at leveraging Prometheus tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Prometheus "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 14 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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About Prometheus MCP Server

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

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.

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.

The Prometheus MCP Server exposes 14 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 14 Prometheus tools available for CrewAI

When CrewAI connects to Prometheus through Vinkius, your AI agent gets direct access to every tool listed below — spanning prometheus, promql, metrics, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

clean

Clean tombstones on Prometheus

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

create

Create snapshot on Prometheus

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

delete

Delete series on Prometheus

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

find

Find series on Prometheus

Find time series matching label selectors

get

Get label values on Prometheus

Get all values for a specific label

get

Get labels on Prometheus

Get a list of all label names

get

Get metadata on Prometheus

Get metadata about metrics scraped from targets

get

Get status buildinfo on Prometheus

Get Prometheus build information

get

Get status config on Prometheus

Get the currently loaded Prometheus configuration (YAML)

get

Get status flags on Prometheus

Get configured Prometheus flag values

get

Get status runtimeinfo on Prometheus

Get Prometheus runtime information

get

Get status tsdb on Prometheus

Get TSDB cardinality statistics

action

Query on Prometheus

Evaluate a PromQL expression at a single point in time

query

Query range on Prometheus

Evaluate a PromQL expression over a range of time

Connect Prometheus to CrewAI via MCP

Follow these steps to wire Prometheus into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 14 tools from Prometheus

Why Use CrewAI with the Prometheus MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Prometheus through the Model Context Protocol.

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

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

Prometheus + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Prometheus MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Prometheus for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Prometheus, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Prometheus tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Prometheus against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Prometheus in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Prometheus immediately.

"Run an instant query for 'up' to see which targets are currently reachable."

"Show me the average CPU usage for the last 30 minutes using query_range."

"What is the metadata for the metric 'http_requests_total'?"

Troubleshooting Prometheus MCP Server with CrewAI

Common issues when connecting Prometheus to CrewAI through Vinkius, and how to resolve them.

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.

Prometheus + CrewAI FAQ

Common questions about integrating Prometheus MCP Server with CrewAI.

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.