How to Use the Grafana MCP in AutoGen

Q: How do AutoGen agents share Grafana alert data during a debate?

The monitoring agent calls firingalerts and posts the JSON payload to the group chat. The other AutoGen agents then read this shared context to coordinate their diagnostic steps.

Let your AutoGen agents debate active Grafana incidents and coordinate automated mitigation steps.

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Connect Grafana MCP to AutoGen

Create your Vinkius account to connect Grafana to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Grafana with AutoGen

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Coordinate multi-agent incident triage

In AutoGen, you can set up a dedicated triage group. One agent monitors Grafana's `firing_alerts` while another uses `get_dashboard` to analyze the affected metrics panels and verify if the alert is a false positive. They debate the severity of the Grafana outage in real time. This AutoGen setup prevents knee-jerk automated rollbacks by forcing the agents to reach a consensus on the root cause.

Verify data source health via AutoGen debates

When a Grafana incident occurs, an AutoGen security agent and a performance agent might disagree on the fix. By using `list_datasources`, they check which Grafana backends are active and trace the issue to the correct database. This MCP Server provides the raw facts needed to resolve these AutoGen debates. The agents use hard data from Grafana to validate their theories instead of relying on static assumptions.

Search and analyze dashboards dynamically

Your AutoGen coordinator agent uses `search_dashboards` to find relevant visualization layouts during a live Grafana incident. It hands the dashboard details to a specialist agent to extract the exact queries causing bottlenecks. This distributed task handling makes Grafana troubleshooting highly efficient. Each AutoGen agent focuses on one part of the configuration, speeding up the overall time to resolve the outage.

Setup guide

Set up Grafana MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Grafana tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Grafana_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Grafana data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Grafana_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Grafana data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Grafana. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Grafana MCP in AutoGen

The monitoring agent calls `firing_alerts` and posts the JSON payload to the group chat. The other AutoGen agents then read this shared context to coordinate their diagnostic steps.

Yes, you can restrict tool access. For example, you can allow only your triage agent to call `get_dashboard` and `search_dashboards`, while keeping other agents focused on analysis.

You should implement a shared cache or rate-limiting wrapper in your AutoGen setup. This prevents competing agents from spamming `list_datasources` and locking up your Grafana API.

No, this server does not support dashboard creation. It provides read-only access to `get_dashboard` and other inspection tools to keep your production setup secure.

Your Grafana credentials reside safely in the secure Vinkius environment, completely hidden from the LLM. Only the raw JSON responses from tools like `firing_alerts` are shared within the AutoGen conversation.

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