CloudGuard MCP

A read-only Model Context Protocol (MCP) server for AWS resource inspection, misconfiguration detection, and cost estimation. It gives an MCP-compatible agent (Claude, or any other MCP client) a safe, structured way to query EC2, S3, and IAM state and to reason about it across multiple tool calls, without granting the agent any ability to modify or delete infrastructure.

Problem

Answering a question like "which S3 buckets are missing encryption, and what would it cost to fix them" normally means opening the AWS console, checking each bucket by hand, and cross-referencing Cost Explorer manually. An LLM agent cannot do this today because it has no safe, structured interface into a live AWS account. This project builds that interface.

Related MCP server: aws-safe-mcp

Design principles

• Read-only by construction, not by convention. Every AWS call in this project is enforced read-only at two independent layers: the IAM policy attached to the credentials (iam/cloudguard-readonly-policy.json), and an application-level check in aws_client.py that refuses to invoke any boto3 operation not prefixed describe_, get_, or list_, regardless of what the IAM policy allows. Either layer failing independently still results in no mutating call reaching AWS.

• Compound detectors, not just raw tools. Individual tools (list_s3_buckets, get_bucket_encryption_status, ...) are composed into detector functions (find_unencrypted_buckets, run_security_audit, ...) that perform the multi-step orchestration server-side, so a calling agent can ask a compound question in one tool call instead of re-deriving the same chain of calls on every query.

• Cached and throttle-aware. All AWS calls go through a shared, TTL-cached client with exponential backoff on throttling, so repeated or overlapping agent queries don't hammer the AWS API.

• Typed contracts. Every tool input and output is a Pydantic model (schemas.py), not a raw dict, so the tool interface is self-documenting and validated at the boundary.

Architecture

src/cloudguard_mcp/
    aws_client.py       Cached, safety-enforced boto3 wrapper. All AWS
                         calls in the project go through this module.
    schemas.py           Typed request/response models for every tool.
    tools/
        ec2_tools.py      EC2 instance + security group inspection.
        s3_tools.py       S3 bucket, encryption, and public-access checks.
        iam_tools.py      IAM role and inline-policy risk checks.
        cost_tools.py     Cost Explorer spend-by-service queries.
    detectors/
        misconfiguration.py   Compound detectors built by chaining the
                               tools above (e.g. find_unencrypted_buckets).
    server.py             The MCP server: registers every tool/resource
                           above with the MCP protocol via FastMCP.
iam/
    cloudguard-readonly-policy.json   The IAM policy to attach to whatever
                                       credentials run this server.
tests/                    pytest + moto test suite. No real AWS account or
                           credentials are required to run the tests.

Available tools

list_ec2_instances (and the detectors built on it, find_open_security_groups and find_idle_ec2_instances) accept a regions list and scan every region in it concurrently, tagging each returned instance with the region it was found in. This is EC2-specific: S3 (list_s3_buckets) and IAM (list_iam_roles) are account-global AWS APIs, not region-scoped, so those tools are unaffected by multi-region support.

Plus one MCP resource, cloudguard://account/inventory, exposing a browsable snapshot of the account's inspected EC2/S3/IAM state.

Setup

pip install -e ".[dev]"

Running the tests (no AWS account required)

The full test suite runs against moto, an in-memory AWS mock — no real credentials, network access, or cost.

pytest tests/ -v

Running against a real AWS account

1. Create a dedicated IAM user or role and attach the policy in iam/cloudguard-readonly-policy.json.

2. Configure credentials for that identity (e.g. aws configure, or the standard AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_DEFAULT_REGION environment variables).

3. Run the server:

python -m cloudguard_mcp.server

Point an MCP client at this process over stdio to begin issuing tool calls.

Demo

demo/run_demo.py connects a real Claude Opus 5 model to this MCP server over stdio (against a seeded, moto-mocked AWS account, so it's runnable without real AWS credentials) and lets it chain tool calls on its own to answer compound questions. See docs/demo_transcript.md for a full writeup of a real run, including the agent parallelizing independent tool calls, refusing to fabricate a per-bucket cost figure when the data wasn't available, and independently catching that one detector's cost estimate was aggregate account spend rather than per-instance.

What this project does not do

This project does not modify, create, or delete any AWS resource under any circumstance. It does not replace dedicated security posture tools such as AWS Config, Prowler, or ScoutSuite for comprehensive compliance scanning; its scope is deliberately narrow (a handful of common, high-signal misconfigurations) in favor of exposing that scope through a well-designed, agent-composable MCP interface rather than a large, static rule set.