Prometheus MCP Server for LlamaIndexGive LlamaIndex instant access to 14 tools to Clean Tombstones, Create Snapshot, Delete Series, and more
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Prometheus as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The Prometheus MCP Server for LlamaIndex 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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Prometheus. "
"You have 14 tools available."
),
)
response = await agent.run(
"What tools are available in Prometheus?"
)
print(response)
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.
LlamaIndex agents combine Prometheus tool responses with indexed documents for comprehensive, grounded answers. Connect 14 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex
When LlamaIndex 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 LlamaIndex via MCP
Follow these steps to wire Prometheus into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Prometheus MCP Server
LlamaIndex provides unique advantages when paired with Prometheus through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Prometheus tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Prometheus tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Prometheus, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Prometheus tools were called, what data was returned, and how it influenced the final answer
Prometheus + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Prometheus MCP Server delivers measurable value.
Hybrid search: combine Prometheus real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Prometheus to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Prometheus for fresh data
Analytical workflows: chain Prometheus queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Prometheus in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Prometheus to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPrometheus + LlamaIndex FAQ
Common questions about integrating Prometheus MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Explore More MCP Servers
View all →
AlgoDocs
10 toolsAI document extraction orchestration — parse PDFs, images, and Word docs via AI.
Chi-Square Test Engine
1 toolsRun exact Chi-Square independence tests on contingency tables local. Get CPU-guaranteed chi² statistics and p-values for categorical analysis.
Sharetribe
9 toolsEquip your AI agent to autonomously manage your marketplace. Approve listings, transition transaction states, audit user profiles, and moderate reviews.
ReferralHero
12 toolsAutomate viral referral campaigns via ReferralHero — manage subscribers, rewards, and leaderboards with AI.