AWS‑IReveal‑MCP integrates with the following AWS services and functionalities:
Aws Ireveal MCP becomes available to MCP clients through the aws ireveal mcp mcp server. AWS‑IReveal‑MCP integrates with the following AWS services and functionalities:.
AWS‑IReveal‑MCP integrates with the following AWS services and functionalities:
Once connected, the assistant can call these 4 tools directly:
CloudTrail — Management event logs for API activityCloudWatch — Operational logs and ad hoc analysisPrerequisites — The Prerequisites tool exposed by this serverConfiguration — Add the following configuration to your MCP client's settings file:Setup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.
The server reads one environment variable: AWS_PROFILE. Keep credentials in your client's env block or a secrets manager rather than committing them.
Monitoring servers replace the paste-the-stack-trace ritual with an assistant that reads the real event, with all its surrounding context intact. Aws Ireveal MCP sits in that group, and the shape of its toolset — CloudTrail, CloudWatch, Prerequisites among others — tells you what it is really for. Worth comparing against the other monitoring security servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
| Tool | What it does |
|---|---|
| CloudTrail | Management event logs for API activity |
| CloudWatch | Operational logs and ad hoc analysis |
| Prerequisites | The Prerequisites tool exposed by this server. |
| Configuration | Add the following configuration to your MCP client's settings file: |
| Variable | Description | Required |
|---|---|---|
| AWS_PROFILE | Configuration value read at startup. | Optional |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.