Introducing AWS MCP Servers for code assistants (Half 1)

We’re excited to announce the open supply launch of AWS MCP Servers for code assistants — a set of specialised Mannequin Context Protocol (MCP) servers that convey Amazon Web Services (AWS) greatest practices on to your improvement workflow. Our specialised AWS MCP servers mix deep AWS information with agentic AI capabilities to speed up improvement throughout key areas. Every AWS MCP Server focuses on a selected area of AWS greatest practices, working collectively to supply complete steering all through your improvement journey.

This publish is the primary in a sequence protecting AWS MCP Servers. On this publish, we stroll by means of how these specialised MCP servers can dramatically cut back your improvement time whereas incorporating safety controls, price optimizations, and AWS Well-Architected greatest practices into your code. Whether or not you’re an skilled AWS developer or simply getting began with cloud improvement, you’ll uncover tips on how to use AI-powered coding assistants to sort out widespread challenges akin to advanced service configurations, infrastructure as code (IaC) implementation, and information base integration. By the top of this publish, you’ll perceive tips on how to begin utilizing AWS MCP Servers to rework your improvement workflow and ship higher options, quicker.

If you wish to get began immediately, skip forward to the part “From Idea to working code in minutes.”

AI is remodeling how we construct software program, creating alternatives to dramatically speed up improvement whereas enhancing code high quality and consistency. Right this moment’s AI assistants can perceive advanced necessities, generate production-ready code, and assist builders navigate technical challenges in actual time. This AI-driven method is especially priceless in cloud improvement, the place builders have to orchestrate a number of companies whereas sustaining safety, scalability, and cost-efficiency.

Builders want code assistants that perceive the nuances of AWS companies and greatest practices. Specialised AI brokers can handle these wants by:

Offering contextual steering on AWS service choice and configuration
Optimizing compliance with safety greatest practices and regulatory necessities
Selling probably the most environment friendly utilization and cost-effective options
Automating repetitive implementation duties with AWS particular patterns

This method means builders can give attention to innovation whereas AI assistants deal with the undifferentiated heavy lifting of coding. Whether or not you’re utilizing Amazon Q, Amazon Bedrock, or different AI instruments in your workflow, AWS MCP Servers complement and improve these capabilities with deep AWS particular information that can assist you construct higher options quicker.

Mannequin Context Protocol (MCP) is a standardized open protocol that allows seamless interplay between large language models (LLMs), knowledge sources, and instruments. This protocol permits AI assistants to make use of specialised tooling and to entry domain-specific information by extending the mannequin’s capabilities past its built-in information—all whereas retaining delicate knowledge native. By means of MCP, general-purpose LLMs can now seamlessly entry related information past preliminary coaching knowledge and be successfully steered in direction of desired outputs by incorporating particular context and greatest practices.

Speed up constructing on AWS

What in case your AI assistant may immediately entry deep AWS information, understanding each AWS service, greatest apply, and architectural sample? With MCP, we will remodel general-purpose LLMs into AWS specialists by connecting them to specialised information servers. This opens up thrilling new potentialities for accelerating cloud improvement whereas sustaining safety and following greatest practices.

Construct on AWS in a fraction of the time, with greatest practices robotically utilized from the primary line of code. Skip hours of documentation analysis and instantly entry ready-to-use patterns for advanced companies akin to Amazon Bedrock Knowledge Bases. Our MCP Servers will aid you write well-architected code from the beginning, implement AWS companies accurately the primary time, and deploy options which can be safe, observable, and cost-optimized by design. Rework the way you construct on AWS as we speak.

Implement AWS greatest practices robotically – Write well-architected code from the beginning with built-in safety controls, correct observability, and optimized useful resource configurations
Minimize analysis time dramatically – Cease spending hours studying documentation. Our MCP Servers present contextually related steering for implementing AWS companies accurately, addressing widespread pitfalls robotically
Entry ready-to-use patterns immediately – Use pre-built AWS CDK constructs, Amazon Bedrock Agents schema mills, and Amazon Bedrock Information Bases integration templates that comply with AWS greatest practices from the beginning
Optimize price proactively – Forestall over-provisioning as you design your resolution by getting cost-optimization suggestions and producing a complete price report to investigate your AWS spending earlier than deployment

To show this imaginative and prescient into actuality and make AWS improvement quicker, safer, and extra environment friendly, we’ve created AWS MCP Servers—a set of specialised AWS MCP Servers that convey AWS greatest practices on to your improvement workflow. Our specialised AWS MCP Servers mix deep AWS information with AI capabilities to speed up improvement throughout key areas. Every AWS MCP Server focuses on a selected area of AWS greatest practices, working collectively to supply complete steering all through your improvement journey.

Overview of domain-specific MCP Servers for AWS improvement

Our specialised MCP Servers are designed to cowl distinct elements of AWS improvement, every bringing deep information to particular domains whereas working in live performance to ship complete options:

Core – The muse server that gives AI processing pipeline capabilities and serves as a central coordinator. It helps present clear plans for constructing AWS options and might federate to different MCP servers as wanted.
AWS Cloud Development Kit (AWS CDK) – Delivers AWS CDK information with instruments for implementing greatest practices, safety configurations with cdk-nag, Powertools for AWS Lambda integration, and specialised constructs for generative AI companies. It makes certain infrastructure as code (IaC) follows AWS Nicely-Architected rules from the beginning.
Amazon Bedrock Information Bases – Allows seamless entry to Amazon Bedrock Information Bases so builders can question enterprise information with pure language, filter outcomes by knowledge supply, and use reranking for improved relevance.
Amazon Nova Canvas – Gives picture era capabilities utilizing Amazon Nova Canvas by means of Amazon Bedrock, enabling the creation of visuals from textual content prompts and coloration palettes—good for mockups, diagrams, and UI design ideas.
Value – Analyzes AWS service prices and generates complete price experiences, serving to builders perceive the monetary implications of their architectural choices and optimize for cost-efficiency.

Stipulations

To finish the answer, it is advisable to have the next conditions in place:

uv package deal supervisor
Set up Python utilizing uv python set up 3.13
AWS credentials with applicable permissions
An MCP-compatible LLM consumer (akin to Anthropic’s Claude for Desktop, Cline, Amazon Q CLI, or Cursor)

From idea to working code in minutes

You may obtain the AWS MCP Servers on GitHub or by means of the PyPI package deal supervisor. Right here’s tips on how to get began utilizing your favourite code assistant with MCP help.

To put in MCP Servers, enter the next code:

# Set up and setup the MCP servers
{
  "mcpServers": {
    "awslabs.core-mcp-server": {
      "command": "uvx",
      "args": [
        "awslabs.core-mcp-server@latest"
      ],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR",
        "MCP_SETTINGS_PATH": "path to your mcp server settings"
      },
      "autoApprove": [],
      "disabled": false
    },
    "awslabs.bedrock-kb-retrieval-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.bedrock-kb-retrieval-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1"
      }
    },
    "awslabs.cdk-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.cdk-mcp-server@latest"],
      "env": {
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    },
    "awslabs.cost-analysis-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.cost-analysis-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "FASTMCP_LOG_LEVEL": "ERROR"
      }
    },
    "awslabs.nova-canvas-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.nova-canvas-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "your-aws-profile",
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

AWS MCP Servers in motion

Right here’s how AWS MCP servers remodel the event expertise:

Developer: “I have to construct an AI-powered chatbot utilizing Amazon Bedrock that may reply questions from our firm’s information base. I additionally wish to add a software for the chatbot to name our inside API.”

Core: “I’ll aid you construct an Amazon Bedrock Information Bases chatbot with API integration. Let’s create an structure that makes use of Amazon Bedrock Brokers with a customized motion group to name your inside API.”

Core generates a complete structure diagram exhibiting the information base integration, Amazon Bedrock Brokers configuration with motion teams, API connectivity, and knowledge move between parts.

AWS CDK: “Right here’s the infrastructure code to your chatbot with the Amazon Bedrock Brokers motion group. I’ve included correct IAM roles, safety controls, and Lambda Powertools for observability.”

// CDK code with Nicely-Architected greatest practices
 const knowledgeBase = new BedrockKnowledgeBase(this, 'CompanyKB', {
   embeddingModel: BedrockFoundationModel.TITAN_EMBED_TEXT_V1,
   vectorStore: new OpenSearchServerlessVectorStore(this, 'VectorStore', {
     encryption: OpenSearchEncryption.KMS,
     ebs: OpenSearchEbsOptions.provisioned(100, OpenSearchVolumeType.GP3)
   })
 });

 // Motion Group for API integration
 const actionGroupSchema = new BedrockAgentSchema(this, 'ApiActionSchema', {
   schemaPath: 'schemas/api-action-schema.json',
   // Schema will likely be auto-generated out of your Lambda code
   generateFromLambda: {
     lambdaPath: 'lambda/api-actions.py',
     outputPath: 'schemas/api-action-schema.json'
   }
 });

 const apiActionGroup = new BedrockAgentActionGroup(this, 'ApiActionGroup', {
   actionGroupName: 'InternalApiActions',
   description: 'Actions to work together with inside API',
   actionGroupExecutor: new BedrockAgentLambdaExecutor(apiActionsLambda),
   actionGroupSchema: actionGroupSchema
 });

The CDK MCP Server generates full AWS CDK code to deploy the complete resolution. It robotically runs cdk-nag to determine potential safety points and gives remediation steps for every discovering, ensuring that the infrastructure follows AWS Nicely-Architected greatest practices.

Amazon Bedrock Information Bases retrieval: “I’ve configured the optimum settings to your information base queries, together with correct reranking for improved relevance.”

Amazon Bedrock Information Bases MCP Server demonstrates tips on how to construction queries to the information base for optimum relevance, gives pattern code for filtering by knowledge supply, and exhibits tips on how to combine the information base responses with the chatbot interface.

Amazon Nova Canvas: “To reinforce your chatbot’s capabilities, I’ve created visualizations that may be generated on demand when customers request knowledge explanations.”

Amazon Nova Canvas MCP server generates pattern pictures exhibiting how Amazon Nova Canvas can create charts, diagrams, and visible explanations based mostly on information base content material, making advanced data extra accessible to customers.

Value Evaluation: “Based mostly in your anticipated utilization patterns, right here’s the estimated month-to-month price breakdown and optimization suggestions.”

The Value Evaluation MCP Server generates an in depth price evaluation report exhibiting projected bills for every AWS service, identifies price optimization alternatives akin to reserved capability for Amazon Bedrock, and gives particular suggestions to cut back prices with out impacting efficiency.

With AWS MCP Servers, what would usually take days of analysis and implementation is accomplished in minutes, with higher high quality, safety, and cost-efficiency than handbook improvement in that very same time.

Greatest practices for MCP-assisted improvement

To maximise the advantages of MCP assisted improvement whereas sustaining safety and code high quality, builders ought to comply with these important tips:

All the time assessment generated code for safety implications earlier than deployment
Use MCP Servers as accelerators, not replacements for developer judgment and experience
Maintain MCP Servers up to date with the newest AWS safety greatest practices
Comply with the precept of least privilege when configuring AWS credentials
Run safety scanning instruments on generated infrastructure code

Developing within the sequence

This publish launched the foundations of AWS MCP Servers and the way they speed up AWS improvement by means of specialised, AWS particular MCP Servers. In upcoming posts, we’ll dive deeper into:

Detailed walkthroughs of every MCP server’s capabilities
Superior patterns for integrating AWS MCP Servers into your improvement workflow
Actual-world case research exhibiting AWS MCP Servers’ affect on improvement velocity
The best way to prolong AWS MCP Servers with your personal customized MCP servers

Keep tuned to learn the way AWS MCP Servers can remodel your particular AWS improvement eventualities and aid you construct higher options quicker. Go to our GitHub repository or Pypi package manager to discover instance implementations and get began as we speak.

In regards to the Authors

Jimin Kim is a Prototyping Architect on the AWS Prototyping and Cloud Engineering (PACE) crew, based mostly in Los Angeles. With specialties in Generative AI and SaaS, she loves serving to her clients succeed of their enterprise. Exterior of labor, she cherishes moments together with her spouse and three lovable calico cats.

Pranjali Bhandari is a part of the Prototyping and Cloud Engineering (PACE) crew at AWS, based mostly within the San Francisco Bay Space. She makes a speciality of Generative AI, distributed methods, and cloud computing. Exterior of labor, she loves exploring numerous mountain climbing trails, biking, and having fun with high quality household time together with her husband and son.

Laith Al-Saadoon is a Principal Prototyping Architect on the Prototyping and Cloud Engineering (PACE) crew. He builds prototypes and options utilizing generative AI, machine studying, knowledge analytics, IoT & edge computing, and full-stack improvement to resolve real-world buyer challenges. In his private time, Laith enjoys the outside–fishing, images, drone flights, and mountain climbing.

Paul Vincent is a Principal Prototyping Architect on the AWS Prototyping and Cloud Engineering (PACE) crew. He works with AWS clients to convey their revolutionary concepts to life. Exterior of labor, he loves enjoying drums and piano, speaking with others by means of Ham radio, all issues residence automation, and film nights with the household.

Justin Lewis leads the Rising Expertise Accelerator at AWS. Justin and his crew assist clients construct with rising applied sciences like generative AI by offering open supply software program examples to encourage their very own innovation. He lives within the San Francisco Bay Space along with his spouse and son.

Anita Lewis is a Technical Program Supervisor on the AWS Rising Expertise Accelerator crew, based mostly in Denver, CO. She makes a speciality of serving to clients speed up their innovation journey with generative AI and rising applied sciences. Exterior of labor, she enjoys aggressive pickleball matches, perfecting her golf recreation, and discovering new journey locations.