# Amazon DynamoDB Table MCP for AI Agents AI Agent Connect

> Amazon DynamoDB Table MCP lets your AI agent interact with a single, specific NoSQL table. It gives your agent a secure way to store and fetch structured data without exposing your entire AWS infrastructure. It's perfect for things like chat history or session state.

## Overview
- **Category:** industry-titans
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_n32klzizAPnc4YO7K3qG23La7StLiOV91J7uGn2a/ai-agent-connect
- **Tags:** nosql, aws, database-management, data-storage, security-scoping, serverless

## Description

This Connector gives your AI agent a dedicated workspace in your AWS environment. Instead of letting an agent roam your whole cloud setup, this tool locks it into one specific DynamoDB table. It's built for people who need a reliable way to store structured data like user profiles, app states, or long-term memories for an agent without worrying about security risks. You don't have to worry about the agent accidentally deleting a production database or seeing data it shouldn't. By connecting this through the Vinkius catalog, you get a secure bridge that lets your agent handle complex NoSQL queries and updates on the fly. It's about giving the agent just enough power to do the job. If you need to keep your data organized and accessible while keeping your permissions tight, this is the way to go. It works by mapping specific actions to your table, meaning your agent can fetch a user profile, update a status, or save a new record without needing a custom backend for every single task. You get a scalable NoSQL database that your agent can interact with as naturally as it talks to you. It's a plug and play solution that handles the heavy lifting of the AWS SDK so you can focus on your application logic. Whether you're managing a high-traffic web app or a simple personal project, this tool ensures your AI agent has the data it needs to be useful.

## Tools

### get_item
Pull a single record from the table using its unique key. It's the fastest way to grab one specific piece of data.

### put_item
Add a new record to your table. Use this when your agent needs to save a new piece of information or a user update.

### query_table
Find multiple items that match specific criteria. This is great for searching for groups of records like all active users.

### scan_table
Read every single item in the table. Use this when you need to see the full picture of your data for analysis.

### delete_item
Remove a specific record from your DynamoDB table. This helps keep your data clean by getting rid of old entries.

## Prompt Examples

**Prompt:** 
```
Find the user with ID 502 and tell me their last login.
```

**Response:** 
```
I've pulled the details for user 502. Here is what I found:

| Field | Value |
| :--- | :--- |
| **Name** | Alice Johnson |
| **Status** | Active |
| **Last Login** | 2023-10-01 |
| **Account Type** | Premium |
```

**Prompt:** 
```
Save a new order for customer 99 with a total of $50.
```

**Response:** 
```
Done! I've recorded that new order for you.

**Order Summary:**
- **Order ID:** ORD-99
- **Customer ID:** 99
- **Total Amount:** $50.00
- **Status:** Pending
```

**Prompt:** 
```
Show me all users who haven't logged in since January.
```

**Response:** 
```
I've finished scanning the table for inactive accounts. I found **4 users** who haven't logged in since January:

* User ID 102
* User ID 445
* User ID 882
* User ID 901

Would you like me to archive these records or just keep the list as is?
```

## Capabilities

### Save new records
Put new entries into your DynamoDB table instantly.

### Fetch specific items
Retrieve a single record using its unique identifier.

### Run complex queries
Find multiple records that match specific criteria or filters.

### Delete old records
Remove specific items from your table to keep your data clean.

### Scan the entire table
Read every record in the table for full data analysis.

## Use Cases

### Long-term Chat Memory
An agent needs to remember a user's preferences. It uses put_item to save a profile and get_item to load it back during a new session.

### Order Tracking
A customer service bot needs to check order status. It uses query_table to find all orders for a specific customer ID.

### User Management Cleanup
An admin tool needs to clean up accounts. The agent uses scan_table to find inactive users and delete_item to remove them.

### Game State Persistence
A game bot needs to save player progress. It uses put_item every time a player completes a level to keep the state updated in DynamoDB.

## Benefits

- Strict security boundaries mean your agent can't touch other tables or delete your production data.
- You get native DynamoDB performance for handling high-volume NoSQL queries and indexes.
- It works as a plug and play database for storing agent memories and application states.
- You don't have to write extra code to handle the AWS SDK for every single request.
- The agent can perform complex queries to filter and sort data without you needing to manually prep the results.
- It provides a scalable way to store structured information that grows with your user base.

## How It Works

The bottom line is you get a secure, isolated way for your agent to handle NoSQL data.

1. Create your specific DynamoDB table in your AWS console.
2. Connect the Connector and provide the necessary scoped credentials.
3. Ask your agent to save or find data in that specific table.

## Frequently Asked Questions

**What does the Amazon DynamoDB Table MCP do?**
This Connector gives your agent the ability to read, write, and query data in one specific DynamoDB table. It's designed to let your agent handle structured data without giving it access to your entire AWS account.

**Is it safe to give my AI agent access to my AWS data?**
Yes, because this Connector is scoped to a single table. It prevents your agent from seeing or touching any other databases, tables, or infrastructure in your account.

**Can I use this for chat history?**
That is a perfect use case. You can have your agent save new messages to the table and retrieve past conversations to maintain context during a session.

**Does it work with any AI client?**
Yes, you can connect this Connector to any compatible client like Claude, Cursor, or Windsurf through the Vinkius catalog.

**How many tables can I connect at once?**
This specific MCP is designed to manage one table at a time. This is a security feature that ensures your agent stays within a strictly defined data boundary.

**Can my agent delete my whole database?**
No. Because the permissions are scoped only to the items within one specific table, your agent cannot drop tables, delete other tables, or modify your cloud infrastructure.

**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.