How do I get my AWS Access Key and Secret Key?

Log in to the AWS Management Console, navigate to IAM (Identity and Access Management), and create a user with programmatic access. Ensure this user has S3 permissions (e.g., `AmazonS3FullAccess`). You will receive an Access Key ID and a Secret Access Key.

Can I upload large files using this integration?

The `put_object` tool handles standard REST uploads and is best suited for small to medium-sized files or JSON data. For very large files, standard AWS tools utilizing Multipart Uploads are recommended.

How do I ensure my buckets are secure?

You can use the `get_bucket_policy` and `get_bucket_acl` tools to audit the current access controls applied to any of your buckets directly from the chat.

Amazon S3 MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Amazon S3
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Amazon S3 as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 Amazon S3. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Amazon S3?"
    )
    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 Amazon S3 MCP Server

Connect your Amazon S3 environment to your AI agent to unlock professional cloud storage orchestration. From creating and auditing buckets to managing individual objects and their metadata, your agent handles your AWS data storage through natural conversation.

LlamaIndex agents combine Amazon S3 tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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

Bucket Orchestration — List your S3 buckets, create new ones, and retrieve their location or policy configurations
Object Management — List objects within a specific bucket, including their size and last modified timestamps
Data Ingestion — Upload objects directly to S3 or delete unwanted files to maintain your storage hygiene
Metadata Auditing — Retrieve technical metadata (headers, content type, size) for specific objects without downloading them
Security Oversight — Audit bucket ACLs and policies to ensure your cloud storage meets compliance requirements

The Amazon S3 MCP Server exposes 10 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 Amazon S3 to LlamaIndex via MCP

Follow these steps to integrate the Amazon S3 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 10 tools from Amazon S3

Why Use LlamaIndex with the Amazon S3 MCP Server

LlamaIndex provides unique advantages when paired with Amazon S3 through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Amazon S3 tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Amazon S3 tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Amazon S3, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Amazon S3 tools were called, what data was returned, and how it influenced the final answer

Amazon S3 + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Amazon S3 MCP Server delivers measurable value.

Hybrid search: combine Amazon S3 real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Amazon S3 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 Amazon S3 for fresh data

Analytical workflows: chain Amazon S3 queries with LlamaIndex's data connectors to build multi-source analytical reports

Amazon S3 MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Amazon S3 to LlamaIndex via MCP:

create_bucket

Create an S3 bucket

delete_bucket

Delete an S3 bucket

delete_object

Delete an object

get_bucket_acl

Get bucket ACL

get_bucket_policy

Get bucket policy

get_object_data

Get object content

get_object_metadata

Get object metadata

list_buckets

List S3 buckets

list_objects

Can be filtered by prefix. List objects in bucket

put_object

Upload an object

Example Prompts for Amazon S3 in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Amazon S3 immediately.

"List all S3 buckets in my account."

"Show the top 10 objects in bucket 'data-lake-raw' starting with prefix '2026/03/'."

"Get the bucket policy for 'website-images-eu'."

Troubleshooting Amazon S3 MCP Server with LlamaIndex

Common issues when connecting Amazon S3 to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Amazon S3 + LlamaIndex FAQ

Common questions about integrating Amazon S3 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 Amazon S3 tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.