How to Use the Dokku MCP in LlamaIndex

Index your Dokku logs and configurations into LlamaIndex. Build RAG apps that know your infrastructure status.

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Connect Dokku MCP to LlamaIndex

Create your Vinkius account to connect Dokku 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 Dokku with LlamaIndex

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Query your Dokku infrastructure

Turn your server state into searchable knowledge. Use `list_apps` to populate your vector store with active service definitions, allowing you to ask questions about your running container fleet. LlamaIndex anchors your answers in real data. Instead of guessing, your agent searches through the output of `list_config` to tell you exactly which environment variables are currently active.

Context-aware Dokku log analysis

Index your application logs to perform semantic analysis. By using `get_logs`, you feed raw container output into your index, making past crashes searchable and explainable. This turns your logs into a reference library. You can query your knowledge base to find patterns in service behavior before running `ps_restart` to apply a fix.

Automated infrastructure maintenance

Build agents that perform maintenance based on indexed data. Use `run_command` to execute tasks inside ephemeral containers, then index the results for future reference. This keeps your operations documented. When you need to check a migration status or a script output, you search your existing knowledge base instead of re-running the command.

Setup guide

Set up Dokku 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 Dokku 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 Dokku tools.",
)
response = await agent.run("List recent Dokku data")

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Common questions about Dokku MCP in LlamaIndex

It uses the MCP tools to fetch live data from your server. That data is then parsed and added to your vector index for retrieval.

Only if you explicitly query it. The server acts as the data source, and LlamaIndex stores the results you choose to index.

Yes. Once you pull logs via the MCP server, they become part of your index. You can then search across historical container output.

The server handles all authentication via an endpoint token. Your secrets are never exposed to the index unless you specifically request them.

You can index your configuration history. This lets you track changes to your app state over time by comparing indexed `list_config` outputs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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