Amazon Quick Suite now enables organizations to connect their AI assistants directly to enterprise applications through a standardized protocol called Model Context Protocol (MCP). This capability addresses a critical challenge: allowing AI agents to securely access organizational data and take actions across multiple business systems without requiring complex custom integrations.
MCP functions as a universal translator between AI systems and enterprise applications. Instead of building individual connections between your AI assistant and each business tool—a process that traditionally required extensive custom development—MCP provides a single, standardized approach that works across platforms. This matters because it dramatically reduces the technical complexity and time required to give AI agents meaningful access to your business ecosystem.
The protocol supports secure authentication methods and enables AI agents to not only read information from enterprise systems but also take actions like creating tasks, updating records, or generating reports. Amazon Quick Suite’s implementation includes native support for popular enterprise applications including Asana, Atlassian’s Jira and Confluence, Box, Canva, HubSpot, Linear, Monday, Notion, PagerDuty, and Zapier, among others.
Amazon Quick Suite includes an MCP client that establishes secure connections between the AI assistant and various enterprise applications through MCP servers. These servers act as intermediaries that translate requests between Amazon Quick Suite and your business applications.
The system supports multiple authentication approaches depending on your security requirements. Three-legged OAuth (3LO) allows individual users to connect their personal accounts to specific applications, ensuring they only access data they’re authorized to see. Two-legged OAuth (2LO) enables service-level authentication for broader organizational access. The platform also supports OAuth 2.0 Dynamic Client Registration, which automatically handles the technical setup process without requiring manual configuration.
For organizations with existing AI agents or custom enterprise solutions, Amazon Quick Suite can connect through Amazon Bedrock AgentCore Gateway, a centralized hub that provides unified access to multiple AI tools and services through a single interface.
Before implementing these integrations, ensure you have the following requirements in place:
Basic AWS setup:
Application-specific requirements:
For advanced integrations using Amazon Bedrock AgentCore:
This integration demonstrates how to connect Amazon Quick Suite with Atlassian’s Jira project management and Confluence documentation platforms. The connection enables your AI assistant to read from and write to both applications while respecting individual user permissions.
Step 1: Access the integrations interface
Sign into Amazon Quick Suite using credentials with Author Pro privileges. From the home screen, navigate to the Connections section in the left sidebar and select “Integrations.” Click the “Actions” tab in the main panel, then select the plus (+) icon in the Model Context Protocol section.
Step 2: Configure the Atlassian connection
On the integration creation screen, provide a descriptive name and description for your Atlassian integration. This description helps other team members understand the integration’s purpose and scope. Enter the Atlassian MCP server endpoint URL: https://mcp.atlassian.com/v1/sse. Click “Next” to proceed.
Step 3: Complete authentication setup
The Atlassian MCP server uses dynamic client registration and three-legged OAuth, meaning Amazon Quick Suite automatically retrieves the necessary connection information from Atlassian’s server. Click “Create and continue” to initiate the authentication process.
A popup window from Atlassian will appear requesting authorization for the Amazon Quick Suite connection. Click “Approve” to grant permission, then enter your Atlassian credentials when prompted.
Step 4: Select your Atlassian instance
Choose the specific Atlassian instance you want to connect to from the available options, then click “Accept.” Note that your Atlassian administrator may need to approve the OAuth client before the connection becomes fully functional.
Amazon Quick Suite will now establish the connection and retrieve available actions from your Atlassian instance. This process typically takes a few minutes as the system discovers what operations are available based on your permissions.
Step 5: Review and share the integration
Once the system displays the available actions, click “Next” to proceed. You can optionally share this integration with other team members and groups. Because the integration uses three-legged OAuth, each user will only be able to access Atlassian content and perform actions they’re personally authorized for in your Atlassian instance. Click “Done” to complete the setup.
Step 6: Test the integration
To verify the connection works correctly, open Amazon Quick Suite’s default chat agent (called “My Assistant”) and enter a prompt such as: “List the Confluence spaces and Jira projects I am authorized to access.” The system will prompt you to sign in and authorize the connection on your behalf, completing the three-legged OAuth process.
The response will display details of the Jira projects and Confluence spaces you have access to, confirming the integration is working properly.
To illustrate the integration’s capabilities, consider a team manager preparing to welcome a new employee. Using the connected Atlassian system, they can streamline the entire onboarding process through natural language commands.
Finding onboarding materials:
The manager starts by locating existing onboarding resources with a simple prompt: “Find new team member checklist for the AnyOrgApp project from Confluence.” The AI assistant searches through accessible Confluence spaces and returns the relevant onboarding checklist.
Creating structured tasks:
Next, the manager converts the checklist into actionable tasks: “Make a list of tasks with start date and due date for a new team member Mateo Jackson joining the team on Monday October 6th, 2025. Create these tasks in Jira project ‘AnyOrgApp-Development’ with start date and due date and assign those to [email protected].”
When the integration attempts to create tasks in Jira, it requests action review for each item being created. This security feature ensures users maintain control over actions that modify business data.
Generating communication:
Finally, the manager can request a welcoming email: “Write a detailed email welcoming Mateo Jackson to the team based on this thread. Use a warm and helpful tone.” The AI assistant generates a comprehensive welcome message that includes the prioritized task list and guidance for getting help from team members.
The AWS Knowledge MCP Server provides access to up-to-date AWS documentation, code samples, and official AWS content. This integration is particularly valuable for teams working with AWS services who need quick access to authoritative technical information.
Step 1: Create the AWS Knowledge integration
Navigate to the Integrations section as described previously and select “Model Context Protocol” for a new integration. Enter the AWS Knowledge Server URL: https://knowledge-mcp.global.api.aws, then click “Next.”
Step 2: Complete the no-authentication setup
The AWS Knowledge Server doesn’t require authentication, so the MCP client has all necessary information to create the integration immediately. Click “Create and continue” to proceed.
Step 3: Review available actions
The system will display the actions made available by the AWS Knowledge Server. These typically include searching AWS documentation, retrieving code samples, and accessing best practices guides. Click “Next” to complete the integration setup.
Step 4: Test AWS Knowledge access
Once the integration status shows as available, test it using Amazon Quick Suite’s chat agent with prompts like: “Get details of the AWS Well-Architected Framework from AWS Knowledge MCP Server and summarize for a CXO presentation in the context of the new SaaS application your company is building on AWS. Use 200 words or fewer.”
Combining multiple integrations:
You can invoke multiple integrations in a single prompt. For example: “Get details of Well-Architected Framework from AWS Knowledge MCP Server and the details of the AnyOrgApp project from Confluence. Prepare well-architected tenets for the AnyOrgApp project team. Be crisp.”
Amazon Bedrock AgentCore Gateway serves as a centralized hub for connecting multiple AI agents and enterprise solutions through a unified interface. This approach is particularly valuable when you need to connect existing AI agents or custom enterprise solutions that don’t have direct MCP server implementations.
The gateway architecture enables you to connect various backend systems—such as Amazon Bedrock agents with Amazon Kendra search capabilities, OpenAI-powered agents, or custom enterprise applications—and make them available through a single MCP endpoint.
Step 1: Create the AgentCore Gateway instance
Use the AWS Management Console to create an Amazon Bedrock AgentCore Gateway instance. Follow the Quick Start guide to set up the gateway with an Amazon Cognito authorizer and default AWS Lambda target. This creates the basic infrastructure needed for the integration.
Step 2: Configure authentication
Open the Amazon Cognito user pool associated with your gateway and navigate to “App clients” in the left sidebar. Create a new app client specifically for Amazon Quick Suite by selecting “Machine-to-machine application.” Record the Client ID, Client secret, and token URL—you’ll need these parameters for the Quick Suite integration.
Step 3: Configure gateway permissions
In the Amazon Bedrock AgentCore console, select your gateway and note the Gateway resource URL. Edit the gateway settings to add your new Cognito app client ID to the list of allowed clients. This ensures Amazon Quick Suite can authenticate with your gateway.
Step 4: Customize the gateway schema
The gateway uses a JSON schema to define available tools and their parameters. You can modify this schema to include your specific enterprise agents and solutions. The default schema includes sample tools like weather and time queries, but you can extend it to include IT helpdesk agents, HR assistance, or any custom business logic.
Step 5: Create the Quick Suite integration
Return to Amazon Quick Suite’s integration interface and create a new MCP integration. Use your Gateway resource URL as the MCP server endpoint. Select “Service authentication” (also known as two-legged OAuth) and enter the Client ID, Client secret, and Token URL from your Cognito app client.
Step 6: Test the gateway connection
Once the integration is created, test it using the built-in API testing feature. Select an action from the dropdown menu, enter the required parameters, and submit the request. Successful responses confirm the integration is working correctly.
IT support automation:
Configure the gateway to connect with an IT helpdesk agent powered by Amazon Bedrock and Amazon Kendra. Employees can ask questions like “My computer is not connecting to the network. Please help” and receive troubleshooting guidance from the AI agent that searches through your IT knowledge base.
HR assistance:
Connect an HR agent that can answer questions about employee benefits and policies. Prompts such as “What are the eligibility criteria for an employee to receive health benefits?” route through the gateway to specialized HR AI agents that have access to current policy information.
Multi-agent workflows:
The gateway enables complex workflows that involve multiple specialized agents. For example, a single prompt could trigger both IT troubleshooting and automatic ticket creation in your service management system.
Security and access control:
When implementing MCP integrations, consider your organization’s security requirements. Three-legged OAuth ensures individual users only access data they’re authorized for, while two-legged OAuth provides broader service-level access. Choose the authentication method that aligns with your data governance policies.
User adoption strategies:
Successful implementation requires user education and change management. Provide clear documentation about what each integration can do and create example prompts that demonstrate practical use cases relevant to different job roles.
Monitoring and maintenance:
Regularly review integration usage and performance. Monitor for authentication issues, especially when user permissions change in connected systems. Establish processes for updating integrations when enterprise applications undergo version changes or security updates.
Scaling considerations:
As your organization adopts more MCP integrations, consider creating governance frameworks around integration creation and sharing. Establish naming conventions, documentation standards, and approval processes for new integrations that access sensitive business data.
Amazon Quick Suite’s MCP integration capabilities transform how organizations can leverage AI assistance across their enterprise application ecosystem. By providing standardized, secure connections to business systems, these integrations enable AI agents to become truly productive members of your team rather than isolated tools requiring constant manual data input.
The combination of direct application connections through hosted MCP servers and the flexibility of Amazon Bedrock AgentCore Gateway for custom solutions provides a comprehensive approach to AI-powered business automation. Organizations can start with simple integrations for common applications and gradually expand to more sophisticated multi-agent workflows as their AI adoption matures.
For teams ready to implement these capabilities, the standardized MCP approach significantly reduces the technical complexity traditionally associated with enterprise AI integrations, making advanced AI assistance accessible to organizations without extensive custom development resources.
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