How to Use the Kibana MCP in LlamaIndex

Q: How does LlamaIndex interact with Kibana saved objects?

It calls findsavedobjects to retrieve configurations. These results are then embedded into your vector index for retrieval-augmented generation.

Q: Can I search across my Kibana spaces with LlamaIndex?

Yes, you use listspaces to identify targets. Once identified, you can iterate through them to index their specific saved objects.

Q: How do I filter which Kibana tools are available?

Use the allowedtools filter in your client setup. You can restrict the agent to read-only operations like getsavedobject if you want to prevent changes.

Index your Kibana observability state in LlamaIndex. Build RAG apps that answer questions about your dashboards and policies.

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Connect Kibana MCP to LlamaIndex

Create your Vinkius account to connect Kibana to LlamaIndex 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 Kibana with LlamaIndex

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Index Kibana configurations for RAG

Turn your saved objects into a searchable knowledge base. Use `find_saved_objects` to pull data and feed it into your vector store for semantic retrieval. Your agent now understands your stack. Ask questions like which dashboards use specific index patterns, and the agent finds the answer by querying the objects it indexed.

Query live API data with LlamaIndex

Ground your AI responses in real-time observability data. Instead of guessing, your agent uses `get_space` or `list_agents` to pull current status updates. This keeps your documentation accurate. When the agent summarizes the health of your Elastic Agents, it pulls the actual data from the API through the MCP tools.

Automate technical configuration tasks

Create and manage your observability setup using structured tool calls. Use `create_or_update_role` to enforce security policies across your Kibana spaces. Your agent keeps configurations consistent. It can run `list_roles` to verify existing permissions before applying changes, ensuring you never overwrite critical access levels.

Setup guide

Set up Kibana MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Kibana MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Kibana tools.",
)
response = await agent.run("List recent Kibana data")

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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 Kibana. 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 Kibana MCP in LlamaIndex

It calls `find_saved_objects` to retrieve configurations. These results are then embedded into your vector index for retrieval-augmented generation.

Yes, you use `list_spaces` to identify targets. Once identified, you can iterate through them to index their specific saved objects.

Only if you configure your storage layer that way. The MCP server provides the data, and LlamaIndex determines how long it persists in your vector store.

Use the allowed_tools filter in your client setup. You can restrict the agent to read-only operations like `get_saved_object` if you want to prevent changes.

Data is pulled over an encrypted connection. Your Kibana instance authentication is handled by the server endpoint, ensuring only your authorized agents access the data.

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