Portainer MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Add Endpoint, Authenticate, Create Docker Container, and more

Name: Portainer
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Portainer as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The Portainer MCP Server for LlamaIndex is a standout in the Ship It category — giving your AI agent 6 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python

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 Portainer. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Portainer?"
    )
    print(response)

asyncio.run(main())

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* 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 Portainer MCP Server

Connect your Portainer instance to any AI agent and orchestrate your containerized infrastructure through natural conversation.

LlamaIndex agents combine Portainer tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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

Container Management — List all Docker containers in any environment, create new ones from images, and start existing containers.
Environment Orchestration — Add and manage new local or remote Docker/Kubernetes environments (endpoints) to your Portainer setup.
Admin Control — Initialize admin accounts on fresh installations and authenticate to receive secure JWT tokens.
Configuration Control — Deploy containers with custom configurations, including exposed ports and host settings via JSON.

The Portainer MCP Server exposes 6 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 6 Portainer tools available for LlamaIndex

When LlamaIndex connects to Portainer through Vinkius, your AI agent gets direct access to every tool listed below — spanning docker, kubernetes, container-management, 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.

add

Add endpoint on Portainer

Add a new environment (endpoint) to Portainer

action

Authenticate on Portainer

Authenticate to receive a JWT token

create

Create docker container on Portainer

Create a new Docker container

init

Init admin on Portainer

Initialize Portainer admin password

list

List docker containers on Portainer

List Docker containers in an environment

start

Start docker container on Portainer

Start a Docker container

Connect Portainer to LlamaIndex via MCP

Follow these steps to wire Portainer into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 6 tools from Portainer

Why Use LlamaIndex with the Portainer MCP Server

LlamaIndex provides unique advantages when paired with Portainer through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Portainer tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Portainer tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Portainer, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Portainer tools were called, what data was returned, and how it influenced the final answer

Portainer + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Portainer MCP Server delivers measurable value.

Hybrid search: combine Portainer real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Portainer 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 Portainer for fresh data

Analytical workflows: chain Portainer queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Portainer in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Portainer immediately.

"List all containers in Portainer endpoint 1."

"Create a new container named 'web-server' using the 'nginx:latest' image in endpoint 2."

"Start the container 'redis-cache' in endpoint 1."

Troubleshooting Portainer MCP Server with LlamaIndex

Common issues when connecting Portainer to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Portainer + LlamaIndex FAQ

Common questions about integrating Portainer MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Portainer tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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