Amazon S3 MCP. Manage your entire data lake from chat.
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Amazon S3 MCP Server: Manage cloud storage and data assets via your AI agent. List, create, and delete S3 buckets.
Upload files (`put_object`), retrieve content and metadata, and audit security policies (ACLs/policies) directly from chat. It lets you manage your entire AWS data lifecycle without touching the console.
What your AI agents can do
Create bucket
Creates a new S3 bucket in your account.
Delete bucket
Permanently removes an entire S3 bucket.
Delete object
Removes a specific file (object) from an existing bucket.
List, create, or delete S3 buckets and check their security policies and ACLs.
Upload files (put_object), delete objects (delete_object), or retrieve object content and metadata.
Check an object's technical metadata (size, content type) or retrieve its full content without needing to download it.
Retrieve the full access control list (ACL) or the detailed security policy for any given bucket.
Ask AI about this MCP
Supported MCP Clients
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019d754dcreate bucket
Creates a new S3 bucket in your account.
019d754ddelete bucket
Permanently removes an entire S3 bucket.
019d754ddelete object
Removes a specific file (object) from an existing bucket.
019d754dget bucket acl
Retrieves the Access Control List (ACL) for a specified bucket.
019d754dget bucket policy
Retrieves the full security policy JSON attached to a bucket.
019d754dget object data
Downloads the raw content of a specific object from S3.
019d754dget object metadata
Gets technical details (size, headers, content type) for an object without downloading its content.
019d754dlist buckets
Lists all the S3 buckets currently existing in your account.
019d754dlist objects
Lists objects within a specified bucket, and can filter the results by a prefix.
019d754dput object
Uploads a file (object) to a specified S3 bucket.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Amazon S3, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Amazon S3 MCP Server lets your AI agent manage all your cloud data assets. You can list, create, or delete entire S3 buckets using list_buckets, create_bucket, and delete_bucket. You can also check a bucket's security policies by calling get_bucket_policy or grab its Access Control List (ACL) with get_bucket_acl. Within a bucket, you can list objects with list_objects and upload new files using put_object.
You can permanently delete individual files with delete_object. To get a file's content, use get_object_data; if you just need the size or content type, get_object_metadata handles that without downloading the whole thing. You can manage the entire AWS data lifecycle straight from your chat interface.
How Amazon S3 MCP Works
- 1 Subscribe to the server and provide your AWS Access Key, Secret Key, and Region.
- 2 Your AI client connects to the MCP endpoint, allowing the agent to see your S3 resource structure.
- 3 You ask your agent a question (e.g., 'What is the policy on the
website-images-eubucket?'), and the agent calls the specific tool to get the answer.
The bottom line is: you tell your agent what you need, and it executes the necessary AWS API calls through the secure, controlled server connection.
Who Is Amazon S3 MCP For?
Anyone dealing with structured data storage in AWS. This is for the DevOps engineer who needs to automate bucket audits. It's for the data scientist who needs to list datasets in a data lake from chat. And for the security analyst who has to audit dozens of bucket ACLs manually.
Automates routine bucket audits, verifies object configurations, and manages file cleanups across multiple S3 buckets.
Quickly locates specific datasets or lists object contents in a data lake directory directly from a chat prompt, without writing a full Boto3 script.
Audits bucket access controls and security policies to identify potential data exposure or compliance gaps across the entire storage structure.
What Changes When You Connect
- Audit bucket security and policies instantly. Instead of logging into the AWS console and navigating through IAM, just ask your agent to run
get_bucket_policyon a bucket name to check compliance. - Manage object lifecycles with precision. Need to clean up old logs? Use
list_objectsto find files matching a date prefix, and then calldelete_objectto maintain storage hygiene. - Inspect data without downloading it. Don't need the raw file? Use
get_object_metadatato check the size, content type, or last modified date of an object—it's instant and doesn't waste bandwidth. - Streamline data ingestion. Upload new assets to S3 using
put_objectand immediately confirm they landed in the correct bucket. Perfect for automated content pipelines. - See your whole data inventory. Use
list_bucketsto see every bucket you own, and thenlist_objectsto drill down into the contents of any specific bucket. - Check granular permissions. Use
get_bucket_aclto verify exactly who has read/write access to a bucket, bypassing complex console navigation.
Real-World Use Cases
Investigating Data Leakage
A security analyst needs to know if a specific bucket (sensitive-data) was accidentally made public. They ask their agent to run get_bucket_policy and get_bucket_acl. The agent retrieves the policy and flags any public read permissions, solving the problem in seconds without manual console review.
Data Lake Inventory Check
A data scientist needs a list of all CSV files uploaded last week in the data-lake-raw bucket. They prompt the agent to run list_objects with a date prefix. The agent returns the list of files, sizes, and prefixes, allowing the scientist to select the exact dataset needed.
Archiving Old Logs
The DevOps team finds a bucket full of old, unnecessary log files. They ask the agent to use list_objects to find files older than 90 days, and then use delete_object on the results to clean up the storage, keeping the environment clean.
Verifying Object Structure
A developer needs to confirm that a newly uploaded object (user_profile.jpg) has the correct metadata (e.g., Content-Type: image/jpeg). They ask the agent to run get_object_metadata on the file, getting the required technical details immediately.
The Tradeoffs
Writing complex CLI scripts
Writing a multi-stage Boto3 script that first calls list_buckets, then loops through each bucket to call list_objects, and finally calls get_bucket_policy inside the loop just to check for public access.
→
Let your agent handle the sequence. Just ask: 'Audit all buckets for public access policies.' The agent runs list_buckets and then iteratively calls get_bucket_policy and get_bucket_acl for every bucket, giving you one consolidated report.
Downloading large files for audit
When a security question comes up, manually downloading a 5GB object just to check its header or content type. This wastes time and bandwidth.
→
Use get_object_metadata. This tool pulls only the headers and technical details, letting you check object properties (size, type, last modified) without downloading the actual data payload.
Assuming immediate state changes
Running put_object and immediately running list_objects expecting the new file to appear instantly, leading to race conditions or confusing 'not found' errors.
→
The agent handles the timing. Use put_object and then ask the agent to verify the upload with get_object_metadata on the expected file path to confirm successful persistence and correct headers.
When It Fits, When It Doesn't
Use this if you need to treat your S3 data layer as a conversational endpoint. This means you want to run audits, check policies, or manage objects without writing boilerplate code or navigating the AWS console. You're managing the state of the data. Don't use this if your goal is high-throughput, real-time streaming data processing (look into dedicated event brokers). Also, don't use this if you need to perform complex calculations on the data (like running a Spark job); this is only for object management and retrieval. When you need to check the state or structure of the data, use get_object_metadata or get_bucket_policy. If you need to change the structure, use create_bucket or put_object.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Amazon S3. 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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Policy on every call
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
The AWS Console is a Maze.
Today, checking if a bucket is public is a painful process. You have to navigate to the bucket, click 'Permissions', then click 'Bucket Policy', and then cross-reference that with the 'Access Control' tab to see the ACL. If you're checking 50 buckets, that's 50 sets of clicks, 50 policy views, and a high chance of missing something.
With the Amazon S3 MCP Server, you just ask your agent: 'Audit the public access on all production buckets.' The agent runs `list_buckets`, then systematically calls `get_bucket_policy` and `get_bucket_acl` for each one, giving you a clean, aggregated report instantly.
Amazon S3 MCP Server: Manage Object Data and Policies
Manual data management involves downloading files for inspection, or running separate scripts to check object existence, size, and content type. It's a lot of repetitive, non-value-add steps.
Now, you just ask your agent to get the details. Running `get_object_metadata` gives you the size, content type, and headers for any object without downloading the actual payload. It's faster, cleaner, and keeps your bandwidth free.
Common Questions About Amazon S3 MCP
How do I check if a bucket is public using Amazon S3 MCP Server and the `get_bucket_policy` tool? +
The agent retrieves the full JSON policy and then flags any public read/write statements. The policy is the definitive source for permissions, so checking it is the fastest way to audit public access.
Should I use `get_object_metadata` or `get_object_data` to check a file's size? +
Use get_object_metadata. This tool returns the size (and other headers) without downloading the content, making it much faster and more efficient than downloading the whole file just for the size.
What is the difference between `list_objects` and `list_buckets`? +
list_buckets shows you the list of containers (the buckets) in your account. list_objects shows you the files (the objects) that live inside a specific bucket.
Can Amazon S3 MCP Server help me delete old files? +
Yes. First, use list_objects to find the files matching criteria (e.g., prefix or date). Then, use delete_object on the resulting file paths to clean up the storage.
How do I upload a large file to a specific bucket using the `put_object` tool? +
The put_object tool handles file uploads. For very large files, ensure your AI client or agent uses multi-part upload functionality, which S3 supports, to prevent timeouts and maintain stability.
What credentials do I need to use the Amazon S3 MCP Server, and what should I do if I get an access denied error? +
You need your AWS Access Key, Secret Key, and the AWS Region. If you get an access denied error, verify that the credentials used by your agent have the necessary IAM permissions (e.g., read/write on the target bucket).
Can I filter the results when I use the `list_objects` tool? +
Yes, the list_objects tool accepts prefixes. You can pass a prefix argument to narrow down the list to only the objects that start with a specific folder path or string.
How do I ensure the data I retrieve with `get_object_metadata` is accurate? +
The get_object_metadata tool fetches the technical headers (like Content-Type and size) directly from S3's API. These are accurate representations of the object as stored, not derived by the server.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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