Grafana MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Grafana as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import 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 Grafana. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in Grafana?"
)
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 Grafana MCP Server
Connect your Grafana instance to any AI agent and take full control of your application observability, dashboard inspections, and alerting workflows through natural conversation.
LlamaIndex agents combine Grafana tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through the 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
- Dashboard Discovery Orchestration — Search for Grafana dashboards by title or tag to retrieve unique UIDs and metadata natively within your chat
- Panel & Query Inspection — Retrieve the full configuration of any dashboard by UID, extracting precise PromQL, LogQL, or SQL queries and panel layouts flawlessly
- Data Source Auditing — List all configured data sources including Prometheus, Loki, CloudWatch, and SQL databases to verify connectivity boundaries securely
- Alert Monitoring Oversight — Enumerate active alert rules and retrieve current firing states to monitor system health and resolve incidents synchronously
- Observability Navigation — Analyze specific localized variables decoding active monitoring routes and extracting structural constraints from your Grafana environment
- SRE Command Center — Verify dashboard UIDs and retrieve query strings to debug performance regressions or analyze log patterns using natural language
The Grafana MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Grafana to LlamaIndex via MCP
Follow these steps to integrate the Grafana MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 4 tools from Grafana
Why Use LlamaIndex with the Grafana MCP Server
LlamaIndex provides unique advantages when paired with Grafana through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Grafana tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Grafana tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Grafana, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Grafana tools were called, what data was returned, and how it influenced the final answer
Grafana + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Grafana MCP Server delivers measurable value.
Hybrid search: combine Grafana real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Grafana 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 Grafana for fresh data
Analytical workflows: chain Grafana queries with LlamaIndex's data connectors to build multi-source analytical reports
Grafana MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect Grafana to LlamaIndex via MCP:
firing_alerts
Returns alerting rules that are currently in "firing" state, including their labels and annotations. Get currently firing alerts from Grafana Unified Alerting
get_dashboard
Requires the dashboard UID, which you can get from search_dashboards. Get full dashboard configuration including panels and queries
list_datasources
) are available in this Grafana instance. List all configured data sources in Grafana
search_dashboards
Returns basic info including the UID. To inspect the panels and queries of a dashboard, use get_dashboard with the uid. Search Grafana dashboards by title or tag
Example Prompts for Grafana in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Grafana immediately.
"Search for dashboards tagged with 'production'"
"Show me the queries for dashboard 'abc-123'"
"Are there any firing alerts right now?"
Troubleshooting Grafana MCP Server with LlamaIndex
Common issues when connecting Grafana to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGrafana + LlamaIndex FAQ
Common questions about integrating Grafana 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?
Connect Grafana with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Grafana to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.