Enterprise Secure AWS MCP Server
Enables secure tool discovery and execution on AWS Lambda with role-based access control, command injection defense, and sensitive data redaction, allowing managed CloudWatch log queries and Terraform operations through a standard MCP interface.
README
Enterprise Secure AWS Model Context Protocol (MCP) Server
Production-grade Model Context Protocol (MCP) Server deployed on AWS Lambda & API Gateway with 100% Terraform IaC, zero-trust IAM RBAC, and OWASP LLM Top 10 defense guardrails.
AWS Architecture Overview

Features
- Standard MCP Specs: Full support for Anthropic Model Context Protocol (MCP) Tool Discovery (
/mcp/tools) and Tool Execution (/mcp/tools/call). - IAM Role-Based Access Control (RBAC): Restricts tool execution by role (
sre_read_only,security_auditor,sre_admin). - OWASP LLM02 Defense: Edge sanitization blocking subshell metacharacter injections (
[;&|'$]). - OWASP LLM06 Defense: Automatic output buffer redactor scrubbing AWS access keys (
AKIA...) and PII. - Interactive Web UI Playground: Dark-mode test interface running on
http://localhost:8080(make ui). - Zero Idle Cloud Cost: Provisioned via Terraform with single-command deployment (
make deploy) and instant teardown (make destroy).
Web UI Playground Demo
Below is the automated high-definition interactive demonstration showing RBAC access denial, command injection defense, and successful server restart execution under the sre_admin role:

Prerequisites & System Setup
Before setting up the project, ensure your environment has Python 3.11+, Terraform, and AWS CLI installed.
1. System Dependencies (Linux / Ubuntu / Debian)
sudo apt update && sudo apt install python3-venv python3-pip terraform awscli -y
2. Environment Setup
Clone the repository and install project dependencies into an isolated virtual environment (.venv):
git clone https://github.com/patelketul1230/aws-mcp-enterprise-server.git
cd aws-mcp-enterprise-server
# Create .venv and install dependencies
make install
Repository Structure
aws-mcp-enterprise-server/
├── docs/
│ ├── assets/ # Centralized PNG images & HD animations
│ │ ├── mcp_aws_figma_architecture_diagram.png
│ │ ├── mcp_ui_testing_demo.webp
│ │ ├── mcp_ui_testing_demo.gif
│ │ ├── mcp_ui_playground_success_screenshot.png
│ │ ├── mcp_ui_playground_admin_success_screenshot.png
│ │ └── mcp_ui_playground_blocked_screenshot.png
│ ├── blogs/ # 3 Medium Sub-Blogs ready for publication
│ │ ├── blog_1_1_mcp_decoded.md
│ │ ├── blog_1_2_mcp_rbac_python.md
│ │ └── blog_1_3_mcp_tool_poisoning.md
│ ├── architecture.md # Figma diagrams & sequence flows
│ ├── 5w_and_how.md # 5 Ws + 1 H problem statement & threat model
│ └── demo_execution.log # Verified test execution log
├── src/
│ ├── clients/ # AWS Bedrock Converse API integration client
│ ├── middleware/ # MCPSecurityValidator (RBAC & DLP)
│ ├── tools/ # CloudWatch & Terraform tool modules
│ ├── ui/ # Interactive Web UI Playground (index.html)
│ └── server.py # AWS Lambda & FastAPI server handler
├── terraform/ # 100% Terraform IaC (API Gateway, IAM, Lambda)
├── tests/ # Pytest suite & live verification scripts
├── Makefile # Lifecycle hooks (make deploy / make destroy / make ui)
└── README.md # Project documentation
Quick Start Guide
1. Install Dependencies
make install
2. Run Tests
make test
3. Deploy Infrastructure to AWS
make deploy
Outputs:
- API Endpoint: Dynamic output generated by Terraform
- CloudWatch Log Group:
/aws/mcp/aws-mcp-enterprise-server-dev
4. Launch Interactive Web UI Playground
make ui
Open http://localhost:8080 in your web browser to test tool calls, RBAC roles, and injection defenses visually!
5. Test via Terminal (curl)
# Health Check
curl -s <YOUR_API_ENDPOINT>/health
# Tool Discovery Catalog
curl -s <YOUR_API_ENDPOINT>/mcp/tools
# Valid Tool Call (CloudWatch Query)
curl -s -X POST <YOUR_API_ENDPOINT>/mcp/tools/call \
-H "Content-Type: application/json" \
-H "X-User-Role: sre_read_only" \
-d '{"tool_name": "query_cloudwatch_logs", "arguments": {"log_group": "/aws/lambda/payment-dev", "filter_pattern": "ERROR"}}'
# Command Injection Attack (Returns 403 Forbidden)
curl -s -X POST <YOUR_API_ENDPOINT>/mcp/tools/call \
-H "Content-Type: application/json" \
-H "X-User-Role: sre_read_only" \
-d '{"tool_name": "query_cloudwatch_logs", "arguments": {"log_group": "/aws/lambda/payment-dev; rm -rf /", "filter_pattern": "ERROR"}}'
6. Tear Down AWS Resources
make destroy
Technical Documentation Links
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。