Prometheus MCP Server for LangChainGive LangChain instant access to 14 tools to Clean Tombstones, Create Snapshot, Delete Series, and more
GDPR Free for SubscribersLangChain is the leading Python framework for composable LLM applications. Connect Prometheus through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this MCP Server for LangChain
The Prometheus MCP Server for LangChain is a standout in the Loved By Devs category — giving your AI agent 14 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"prometheus": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Prometheus, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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
About Prometheus MCP Server
Connect your Prometheus instance to any AI agent and transform your observability data into actionable insights through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Prometheus through native MCP adapters. Connect 14 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain
When LangChain 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 tombstones on Prometheus
enable-admin-api to be enabled. Remove deleted data from disk
Create snapshot on Prometheus
enable-admin-api to be enabled on the Prometheus server. Create a snapshot of all current data
Delete series on Prometheus
enable-admin-api to be enabled. Delete data for a selection of series in a time range
Find series on Prometheus
Find time series matching label selectors
Get label values on Prometheus
Get all values for a specific label
Get labels on Prometheus
Get a list of all label names
Get metadata on Prometheus
Get metadata about metrics scraped from targets
Get status buildinfo on Prometheus
Get Prometheus build information
Get status config on Prometheus
Get the currently loaded Prometheus configuration (YAML)
Get status flags on Prometheus
Get configured Prometheus flag values
Get status runtimeinfo on Prometheus
Get Prometheus runtime information
Get status tsdb on Prometheus
Get TSDB cardinality statistics
Query on Prometheus
Evaluate a PromQL expression at a single point in time
Query range on Prometheus
Evaluate a PromQL expression over a range of time
Connect Prometheus to LangChain via MCP
Follow these steps to wire Prometheus into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Prometheus MCP Server
LangChain provides unique advantages when paired with Prometheus through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Prometheus MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Prometheus queries for multi-turn workflows
Prometheus + LangChain Use Cases
Practical scenarios where LangChain combined with the Prometheus MCP Server delivers measurable value.
RAG with live data: combine Prometheus tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Prometheus, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Prometheus tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Prometheus tool call, measure latency, and optimize your agent's performance
Example Prompts for Prometheus in LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Prometheus to LangChain through Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersPrometheus + LangChain FAQ
Common questions about integrating Prometheus MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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