AWS Resources MCP Server

The AWS Resources MCP Server is a lightweight, container‑ready implementation that lets AI assistants like Claude run arbitrary Python code against AWS services via boto3. Rather than relying on a static, read‑only query interface, this server gives the assistant full read/write access governed by the IAM permissions of the AWS credentials it receives. The result is a highly flexible bridge that turns natural‑language requests into executable code, returning structured JSON for downstream processing.

What problem does it solve? Traditional AWS tooling (e.g., the official AWS Chatbot or third‑party MCP servers) often impose strict quotas, limited feature sets, or language constraints that hinder rapid experimentation. Developers who already work in Python find the Node.js ecosystem of some existing MCP servers a barrier to contribution. This server eliminates those friction points by shipping as a Docker image, requiring no local clone or complex setup. It also incorporates sandboxing to ensure that the executed code cannot escape the container, giving ops teams confidence in a controlled environment.

Key features include:

• Dynamic resource endpoint () that exposes all boto3 services without hard‑coding each one.
• A tool () that accepts a code snippet, executes it inside the container, and automatically serialises the variable to JSON. This includes intelligent handling of AWS‑specific types such as dates and ARNs.
• Python‑centric design, making it straightforward for Python developers to extend or tweak the server logic.
• Containerised sandboxing that isolates execution, preventing accidental data exfiltration or privilege escalation beyond the IAM role’s scope.

Typical use cases are abundant. A developer can ask an AI assistant to list all S3 buckets, retrieve the latest CodePipeline execution, or modify a DynamoDB table—all without leaving the chat interface. Operations teams can leverage it to diagnose permission errors, audit resource configurations, or automate remediation steps directly from a conversation. In CI/CD pipelines, the assistant can trigger infrastructure changes or fetch deployment statuses on demand.

Integration into AI workflows is seamless. The server registers its resources and tools via the MCP handshake, allowing any compliant client to discover and invoke them. Because the code is executed in a container with the same IAM credentials used by the client, there’s no need for additional authentication steps; permissions are enforced automatically. The JSON output can be fed back into the assistant for natural‑language summarisation or used by downstream services for further automation.

In summary, the AWS Resources MCP Server empowers developers and ops teams to harness AI assistants for real‑time, secure interaction with AWS services. Its Python foundation, Docker‑based deployment, and robust sandboxing make it a practical, low‑friction alternative to existing solutions, especially for teams that value flexibility and rapid iteration.