DynamoDB Table MCP. Manage isolated, structured data for your AI agent.
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Amazon DynamoDB Table MCP Server lets your AI agent read, write, and manage items in a single DynamoDB table. It gives your agent a secure, read-only scope to query, insert, and update data without touching your broader cloud infrastructure.
This is perfect for giving an AI client a controlled, scalable NoSQL database for storing application state or chat history.
What your AI agents can do
Delete item
Removes a specified item from the DynamoDB table.
Get item
Retrieves a single item from the DynamoDB table using its primary key.
Put item
Adds a new item or overwrites an existing item in the DynamoDB table.
Your AI client fetches a single item from the table using its unique primary key.
Your AI client writes a new item or overwrites an existing item in the table.
Your AI client queries the table, returning multiple items that match specific criteria.
Your AI client scans every item in the table to find data that matches a given pattern.
Your AI client deletes a specified item from the table.
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Supported MCP Clients
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Amazon DynamoDB Table MCP Server: 5 Tools for Data Operations
Perform item deletion, retrieval, insertion, querying, and scanning of table data through these dedicated tools.
019e3862delete item
Removes a specified item from the DynamoDB table.
019e3862get item
Retrieves a single item from the DynamoDB table using its primary key.
019e3862put item
Adds a new item or overwrites an existing item in the DynamoDB table.
019e3862query table
Filters and retrieves multiple items from the DynamoDB table based on provided criteria.
019e3862scan table
Reads and returns every item in the DynamoDB table, useful for full dataset checks.
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
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Make Your AI Do More
Start with Amazon DynamoDB Table, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
This server lets your AI agent read, write, and manage items in a single DynamoDB table. It gives your agent a secure, read-only scope to query, insert, and update data without touching your broader cloud infrastructure. It's perfect for giving your AI client a controlled, scalable NoSQL database for storing application state or chat history.
get_item lets your AI client fetch a single item from the table using its unique primary key.put_item writes a new item or overwrites an existing one in the table.query_table filters and gets multiple items that match specific criteria.scan_table reads and returns every item in the DynamoDB table, useful for full dataset checks.delete_item removes a specified item from the table.
Your AI client can handle structured data, store chat histories, or process complex NoSQL queries without ever touching your main cloud databases. It's absolute containment. When your agent fetches a single item, it uses get_item with the item's primary key. To write data, it uses put_item to either add a new item or overwrite an existing one.
Need a set of matching records? query_table lets your AI client filter the table and get multiple items based on criteria. If you gotta search the whole damn thing, scan_table reads every item in the table. When it's time to clean up, delete_item removes a specific item.
By strictly scoping access to one table, you keep your data operations isolated. Your agent gets a functional, scalable NoSQL database to manage application state or structured memories—all while remaining locked out of your critical production tables. It's a safe, contained data layer.
How DynamoDB Table MCP Works
- 1 Your AI client initiates a request (e.g., 'Get the user profile for ID 123').
- 2 The agent uses the
get_itemtool, passing the necessary ID and parameters. - 3 The server executes the call and returns the requested item data to the agent for processing.
The bottom line is that the server provides a secure, single-table wrapper around DynamoDB, letting your AI client perform specific data operations without needing full AWS permissions.
Who Is DynamoDB Table MCP For?
The application developer building agents needs this. It solves the problem of giving an AI agent persistent memory or a working data store without giving it dangerous, full-access credentials. It's for the engineer who can't afford to accidentally let their agent delete the whole production database.
Builds and tests agents that need to store conversation history, manage application state, or process data inputs reliably. They use this to give their agent a structured 'memory'.
Integrates an AI agent into a microservice. They use this to safely manage session data, user preferences, or lookup records that don't belong in the main transactional database.
Designs the data layer for an AI application. They use this to isolate the unstructured, high-volume data that the agent writes from the core, highly structured business data.
What Changes When You Connect
- Safe Data Storage: The server scopes the AI client to a single table. You don't have to worry about the agent accidentally running commands that hit critical, unrelated cloud databases.
- Item Retrieval: Use the
get_itemtool when your agent needs to fetch a single, specific piece of data—like a user's ID or a chat session's last message. It’s fast and focused. - State Management: The
put_itemtool lets your agent write and update records. This is perfect for giving the agent a persistent 'memory' or tracking application state. - Data Lookup: When the agent needs to find several records that share a common attribute (e.g., all users in 'California'),
query_tablehandles the filtering efficiently. - Bulk Data Analysis: The
scan_tabletool lets you search the entire dataset. Use this when you need to find all records that match a specific pattern across the entire table, but be mindful of performance. - Clean Deletion: The
delete_itemtool allows the agent to clean up stale data or remove records that are no longer needed, keeping your database tidy.
Real-World Use Cases
Building Chat History Storage
A user asks their agent to recall a detail from last week. Instead of relying on ephemeral memory, the agent uses query_table to filter the table by user ID and date range. It then uses get_item to pull the relevant conversation chunks, solving the memory problem without external databases.
Tracking Application State
An agent processes an order. It needs to update the order status from 'Pending' to 'Processing'. The agent uses get_item to pull the existing order record, modifies the status in memory, and then uses put_item to write the updated record back. The state is saved safely.
Auditing and Cleanup
The ops team runs a script to find all records marked 'inactive' older than 90 days. The agent uses scan_table with filters to locate these records, and then runs delete_item on each one, keeping the table clean and reducing storage costs.
User Profile Synchronization
A user updates their email address via a web form. The agent needs to ensure this change is reflected in the stored profile. It uses get_item to pull the old profile, updates the email field, and calls put_item to commit the new, correct record.
The Tradeoffs
Using `scan_table` for everything
Thinking that scan_table is the easiest way to find a record. This forces a full read of the entire table, which is slow, expensive, and will throttle the server under load.
→
First, check if you can use get_item with the primary key. If not, use query_table by specifying the correct partition key and index attributes. Never rely on scan_table for routine lookups.
Assuming atomicity between calls
Calling get_item to read a value, and then calling put_item to write a new value, assuming no one else changed the data in between. This causes lost updates and data corruption.
→ If the application requires guaranteed transactional integrity across multiple steps, you must handle the compensating logic at the application layer, checking for version numbers or implementing explicit locking mechanisms. The tools only provide atomic primitives.
Overwriting data without checking
Calling put_item blindly with a new record, potentially wiping out existing critical data fields because the tool just replaces the whole item.
→
Always use get_item first to inspect the current item structure. Then, use the retrieved data to construct the new item, ensuring you only update the fields that actually need changing.
When It Fits, When It Doesn't
Use this MCP Server if your application needs an AI agent to reliably read, write, or delete structured data in a controlled, isolated NoSQL environment. It's ideal for session storage, chat history, or lightweight state management.
Don't use this if you need complex, multi-item ACID transactions across multiple tables. This toolset provides atomic primitives, not transactional scope. For multi-step, guaranteed consistency, you'll need a dedicated workflow orchestration service (like a Saga pattern implementation).
If you only need simple data retrieval from a known key, get_item is the fastest path. If you need to search based on a filter, use query_table. Only use scan_table when you genuinely need to audit the whole dataset.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Amazon DynamoDB Table. 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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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 5 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Data storage shouldn't require a dozen clicks and a full AWS console login.
Today, giving an AI agent access to data means fighting permissions. You're forced to grant broad, dangerous AWS roles, or you end up with a spaghetti of separate, complex API calls. You spend time managing IAM policies and worrying about what the agent might accidentally touch.
With this MCP Server, you wrap the data layer. You give your agent one safe, single-table superpower. It can read, write, and delete structured items using specific tools, but it can't list other tables or drop your production data. It's surgical access.
Amazon DynamoDB Table MCP Server: Structured Data Operations
You don't write code blocks to manage state anymore. You just ask your agent to 'Update the order status to shipped.' The agent uses `get_item` to pull the current record, updates the status internally, and calls `put_item` to commit the change. Done.
The difference is that data operations become part of the natural conversation. Your agent handles the tool calls and the data flow, letting you focus on the logic, not the plumbing.
Common Questions About DynamoDB Table MCP
How do I use the `get_item` tool with Amazon DynamoDB Table MCP Server? +
You call get_item and provide the primary key (PK) and any required key attributes. This tool retrieves a single item record. It's fast because it only looks up by the known key, not by searching.
Is `scan_table` safe to use often with Amazon DynamoDB Table MCP Server? +
No. scan_table reads every single item in the table. This is slow and expensive. Only use it when you absolutely need to check the entire dataset for a specific pattern. Otherwise, use query_table.
Can I update a record using `put_item` if I only have the item ID? +
You can, but it's safer to use get_item first. put_item replaces the entire item. If you don't read it first, you risk overwriting critical fields that weren't part of your new data payload.
How does `query_table` work on DynamoDB Table? +
query_table lets you find multiple items that match criteria (like a specific user ID and status). It's designed to be more efficient than scan_table because it uses defined indexes.
What should I do if `delete_item` fails due to a conditional write failure? +
You must include a condition expression in your request. This ensures the item only gets deleted if specific attributes match the expected values, preventing accidental deletions.
How can I use `query_table` to filter data by attributes other than the primary key? +
You filter data by adding a FilterExpression to your query request. This narrows the results set after DynamoDB performs the initial lookup based on the primary key.
Is there a limit to how many records I can process using `scan_table`? +
Yes, scan_table reads every item in the table, which is resource-intensive. For large datasets, use pagination and limit batch sizes to manage throughput and costs.
Does `put_item` support atomic updates to multiple attributes? +
Yes, put_item supports atomic operations. You define specific attributes and use update expressions to guarantee that changes happen as a single, indivisible unit.
Why limit the agent to a single table? +
To enforce the principle of least privilege and zero-trust architecture. An autonomous agent shouldn't have the power to alter global cloud databases, which prevents accidental corruption of critical systems.
Can my agent access multiple tables? +
Each instance of this server is scoped to exactly one table. If your agent needs access to multiple tables, you can subscribe to this server multiple times — each with a different table configuration. This maintains strict isolation.
Can I query using secondary indexes (GSI)? +
Yes, you can specify the 'IndexName' inside your expression parameters when using the query tool, allowing the agent to perform efficient lookups on Global Secondary Indexes.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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