Python MCP Server

Generate a Python Model Context Protocol (MCP) server for providing context to Large Language Models (LLMs), and optionally deploy it to Amazon Bedrock AgentCore.

What is MCP?

The Model Context Protocol (MCP) is an open standard that allows AI assistants to interact with external tools and resources. It provides a consistent way for LLMs to:

Execute tools (functions) that perform actions or retrieve information
Access resources that provide context or data

Usage

Generate an MCP Server

You can generate a Python MCP server in two ways:

VSCode
CLI

Install the Nx Console VSCode Plugin if you haven't already
Open the Nx Console in VSCode
Click Generate (UI) in the "Common Nx Commands" section
Search for @aws/nx-plugin - py#mcp-server
Fill in the required parameters
Click Generate

pnpm nx g @aws/nx-plugin:py#mcp-server

yarn nx g @aws/nx-plugin:py#mcp-server

npx nx g @aws/nx-plugin:py#mcp-server

bunx nx g @aws/nx-plugin:py#mcp-server

You can also perform a dry-run to see what files would be changed

pnpm nx g @aws/nx-plugin:py#mcp-server --dry-run

yarn nx g @aws/nx-plugin:py#mcp-server --dry-run

npx nx g @aws/nx-plugin:py#mcp-server --dry-run

bunx nx g @aws/nx-plugin:py#mcp-server --dry-run

Options

Parameter	Type	Default	Description
project Required	string	-	The project to add an MCP server to
computeType	string	BedrockAgentCoreRuntime	The type of compute to host your MCP server. Select None for no hosting.
name	string	-	The name of your MCP server (default: mcp-server)
iacProvider	string	Inherit	The preferred IaC provider. By default this is inherited from your initial selection.

Generator Output

The generator will add the following files to your existing Python project:

Directoryyour-project/
- Directoryyour_module/
  - Directorymcp_server/ (or custom name if specified)
    __init__.py Python package initialization
    server.py Main server definition with sample tools and resources
    stdio.py Entry point for STDIO transport, useful for simple local MCP servers
    http.py Entry point for Streamable HTTP transport, useful for hosting your MCP server
    Dockerfile Entry point for hosting your MCP server (excluded when computeType is set to None)
- pyproject.toml Updated with MCP dependencies
- project.json Updated with MCP server serve targets

Infrastructure

Since this generator vends infrastructure as code based on your chosen iacProvider, it will create a project in packages/common which includes the relevant CDK constructs or Terraform modules.

The common infrastructure as code project is structured as follows:

CDK
Terraform

Directorypackages/common/constructs
- Directorysrc
  - Directoryapp/ Constructs for infrastructure specific to a project/generator
    …
  - Directorycore/ Generic constructs which are reused by constructs in app
    …
  - index.ts Entry point exporting constructs from app
- project.json Project build targets and configuration

Directorypackages/common/terraform
- Directorysrc
  - Directoryapp/ Terraform modules for infrastructure specific to a project/generator
    …
  - Directorycore/ Generic modules which are reused by modules in app
    …
- project.json Project build targets and configuration

For deploying your MCP Server, the following files are generated:

CDK
Terraform

Directorypackages/common/constructs/src
- Directoryapp
  - Directorymcp-servers
    Directory<project-name>
    <project-name>.ts CDK construct for deploying your MCP Server
    Dockerfile Passthrough docker file used by the CDK construct
- Directorycore
  - Directoryagent-core
    runtime.ts Generic CDK construct for deploying to Bedrock AgentCore Runtime

Working with Your MCP Server

Adding Tools

Tools are functions that the AI assistant can call to perform actions. The Python MCP server uses the MCP Python SDK (FastMCP) library, which provides a simple decorator-based approach for defining tools.

You can add new tools in the server.py file:

@mcp.tool(description="Your tool description")
def your_tool_name(param1: str, param2: int) -> str:
    """Tool implementation with type hints"""
    # Your tool logic here
    return f"Result: {param1} with {param2}"

The FastMCP library automatically handles:

Type validation based on your function’s type hints
JSON schema generation for the MCP protocol
Error handling and response formatting

Adding Resources

Resources provide context to the AI assistant. You can add resources using the @mcp.resource decorator:

@mcp.resource("example://static-resource", description="Static resource example")
def static_resource() -> str:
    """Return static content"""
    return "This is static content that provides context to the AI"

@mcp.resource("dynamic://resource/{item_id}", description="Dynamic resource example")
def dynamic_resource(item_id: str) -> str:
    """Return dynamic content based on parameters"""
    # Fetch data based on item_id
    data = fetch_data_for_item(item_id)
    return f"Dynamic content for {item_id}: {data}"

Configuring with AI Assistants

Configuration Files

Most AI assistants that support MCP use a similar configuration approach. You’ll need to create or update a configuration file with your MCP server details:

{
  "mcpServers": {
    "your-mcp-server": {
      "command": "uv",
      "args": [
        "run",
        "python",
        "-m",
        "my_module.mcp_server.stdio"
      ],
      "env": {
        "VIRTUAL_ENV": "/path/to/your/project/.venv"
      }
    }
  }
}

Assistant-Specific Configuration

Please refer to the following documentation for configuring MCP with specific AI Assistants:

Running Your MCP Server

Inspector

The generator configures a target named <your-server-name>-inspect, which starts the MCP Inspector with the configuration to connect to your MCP server using STDIO transport.

pnpm nx run your-project:your-server-name-inspect

yarn nx run your-project:your-server-name-inspect

npx nx run your-project:your-server-name-inspect

bunx nx run your-project:your-server-name-inspect

This will start the inspector at http://localhost:6274. Get started by clicking on the “Connect” button.

STDIO

The easiest way to test and use an MCP server is by using the inspector or configuring it with an AI assistant (as above).

You can however run your server with STDIO transport directly using the <your-server-name>-serve-stdio target.

pnpm nx run your-project:your-server-name-serve-stdio

yarn nx run your-project:your-server-name-serve-stdio

npx nx run your-project:your-server-name-serve-stdio

bunx nx run your-project:your-server-name-serve-stdio

This command uses uv run to execute your MCP server with STDIO transport.

Streamable HTTP

If you would like to run your MCP server locally using Streamable HTTP transport, you can use the <your-server-name>-serve target.

pnpm nx run your-project:your-server-name-serve

yarn nx run your-project:your-server-name-serve

npx nx run your-project:your-server-name-serve

bunx nx run your-project:your-server-name-serve

This command uses uv run to execute your MCP server with HTTP transport, typically running on port 8000.

Deploying Your MCP Server to Bedrock AgentCore Runtime

Infrastructure as Code

If you selected BedrockAgentCoreRuntime for computeType, the relevant CDK or Terraform infrastructure is generated which you can use to deploy your MCP server to Amazon Bedrock AgentCore Runtime.

CDK
Terraform

A CDK construct is generated for your, named based on the name you chose when running the generator, or <ProjectName>McpServer by default.

You can use this CDK construct in a CDK application:

import { MyProjectMcpServer } from ':my-scope/common-constructs';

export class ExampleStack extends Stack {
  constructor(scope: Construct, id: string) {
    // Add the MCP server to your stack
    new MyProjectMcpServer(this, 'MyProjectMcpServer');
  }
}

A Terraform module is generated for you, named based on the name you chose when running the generator, or <ProjectName>-mcp-server by default.

You can use this terraform module in a Terraform project:

# MCP Server
module "my_project_mcp_server" {
  # Relative path to the generated module in the common/terraform project
  source = "../../common/terraform/src/app/mcp-servers/my-project-mcp-server"
}

Docker is required to build and deploy your MCP server. Make sure you have installed and launched Docker to avoid errors such as:

ERROR: Cannot connect to the Docker daemon at unix://path/to/docker.sock.

Authentication

IAM

By default, your MCP server will be secured using IAM authentication, simply deploy it without any arguments:

CDK
Terraform

import { MyProjectMcpServer } from ':my-scope/common-constructs';

export class ExampleStack extends Stack {
  constructor(scope: Construct, id: string) {
    new MyProjectMcpServer(this, 'MyProjectMcpServer');
  }
}

You can grant access to invoke your MCP on Bedrock AgentCore Runtime using the grantInvoke method. For example you may wish for an agent generated with the py#strands-agent generator to call your MCP server:

import { MyProjectAgent, MyProjectMcpServer } from ':my-scope/common-constructs';

export class ExampleStack extends Stack {
  constructor(scope: Construct, id: string) {
    const agent = new MyProjectAgent(this, 'MyProjectAgent');
    const mcpServer = new MyProjectMcpServer(this, 'MyProjectMcpServer');

    mcpServer.agentCoreRuntime.grantInvoke(agent.agentCoreRuntime);
  }
}

# MCP Server
module "my_project_mcp_server" {
  # Relative path to the generated module in the common/terraform project
  source = "../../common/terraform/src/app/mcp-servers/my-project-mcp-server"
}

To grant access to invoke your agent, you will need to add a policy such as the following, referencing the module.my_project_mcp_server.agent_core_runtime_arn output:

{
  Effect = "Allow"
  Action = [
    "bedrock-agentcore:InvokeAgentRuntime"
  ]
  Resource = [
    module.my_project_mcp_server.agent_core_runtime_arn,
    "${module.my_project_mcp_server.agent_core_runtime_arn}/*"
  ]
}

Cognito JWT Authentication

The below demonstrates how to configure Cognito authentication for your agent.

CDK
Terraform

To configure JWT authentication, you can pass the authorizerConfiguration property to your MCP server construct. Here is an example which configures a Cognito user pool and client to secure the MCP server:

import { MyProjectMcpServer } from ':my-scope/common-constructs';

export class ExampleStack extends Stack {
  constructor(scope: Construct, id: string) {
    const userPool = new UserPool(this, 'UserPool');
    const client = userPool.addClient('Client', {
      authFlows: {
        userPassword: true,
      },
    });

    new MyProjectMcpServer(this, 'MyProjectMcpServer', {
      authorizerConfiguration: {
        customJwtAuthorizer: {
          discoveryUrl: `https://cognito-idp.${Stack.of(userPool).region}.amazonaws.com/${userPool.userPoolId}/.well-known/openid-configuration`,
          allowedClients: [client.userPoolClientId],
        },
      },
    });
  }
}

To configure JWT authentication, you can edit your MCP Server module to configure the customJWTAuthorizer variable as follows:

data "aws_region" "current" {}

locals {
  aws_region = data.aws_region.current.name

  # Replace with your user pool and client ids or expose as variables
  user_pool_id = "xxx"
  user_pool_client_ids = ["yyy"]
}

module "agent_core_runtime" {
  source = "../../../core/agent-core"
  agent_runtime_name = "MyProjectMcpServer"
  docker_image_tag = "my-scope-my-project-agent:latest"
  server_protocol = "MCP"
  customJWTAuthorizer = {
    discoveryUrl = "https://cognito-idp.${local.aws_region}.amazonaws.com/${local.user_pool_id}/.well-known/openid-configuration",
    allowedClients = local.user_pool_client_ids
  }
  env = var.env
  additional_iam_policy_statements = var.additional_iam_policy_statements
  tags = var.tags
}

Bundle and Docker Targets

In order to build your MCP server for Bedrock AgentCore Runtime, a bundle target is added to your project, which:

Exports your Python dependencies to a requirements.txt file using uv export
Installs dependencies for the target platform (aarch64-manylinux2014) using uv pip install

A docker target specific to your MCP server is also added, which:

Builds a docker image from the Dockerfile which runs your MCP server on port 8000, as per the MCP protocol contract

Observability

Your MCP server is automatically configured with observability using the AWS Distro for Open Telemetry (ADOT), by configuring auto-instrumentation in your Dockerfile.

You can find traces in the CloudWatch AWS Console, by selecting “GenAI Observability” in the menu. Note that for traces to be populated you will need to enable Transaction Search.

For more details, refer to the AgentCore documentation on observability.