AWS DevOps Study KB
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AWS DevOps Study KBSave a memo comparing gp3 and io2 EBS volumes, tag ebs and storage"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AWS DevOps Study KB
A self-hosted memo knowledge base for AWS DevOps Engineer Professional exam prep — you don't write the notes, your AI agent does. Ask Claude, OpenWebUI, or any other agent a study question ("what's the baseline performance difference between gp3 and io2, and when would I pick each?"), and it saves a well-structured, tagged memo here as a side effect of answering — grounded in official AWS docs if it's also connected to the AWS Knowledge MCP server. Ask again next week and the agent finds the memo instead of re-deriving the answer.
No folders, no manual organizing — every memo is flat, tagged, and full-text searchable. The web UI is read-only by design: agents own all writes.
you: "explain EBS volume types, baseline performance, and when to pick each"
agent: [searches this KB — nothing found] [answers, grounded in AWS docs] [saves a memo,
tagged ebs, storage, exam:resilient-cloud-solutions]
...two weeks later...
you: "remind me about EBS gp3 vs io2"
agent: [searches this KB — finds the memo] "Yep, saved this one already — gp3 baseline is
3,000 IOPS / 125 MiB/s regardless of size..."How it works
One Node.js/TypeScript service exposes the same memo store three ways, so it works with whatever your AI client speaks:
MCP (
/mcp, streamable-HTTP) — for Claude Code, Claude Desktop, OpenWebUI, or any other MCP-native client. Tools:create_memo,search_memos,get_memo,list_tags,update_memo,delete_memo.REST + OpenAPI (
/api/v1/*, spec at/openapi.json) — for agent frameworks and bots that only do OpenAPI/REST tool-calling (custom GPT Actions, LibreChat, a homegrown Telegram bot, etc.).Web UI (
/) — read-only browse/search/tag-filter view for you, gated by a simple cookie-session login. No create/edit forms exist at all — that's not an oversight, it's the point.
All three are thin adapters over one SQLite-backed service (search is FTS5 full-text + tag
filtering), so nothing can drift between what an MCP client sees and what a REST client sees — see
CLAUDE.md for the architecture in more detail.
Quickstart
git clone <this-repo-url>
cd aws-devops-study-kb
cp .env.example .env # fill in API_BEARER_TOKEN and SESSION_COOKIE_SECRET (see below)
npm install
npm run dev # http://localhost:8000Generate real secrets rather than leaving the .env.example placeholders:
openssl rand -hex 32 # → API_BEARER_TOKEN
openssl rand -hex 32 # → SESSION_COOKIE_SECRETFor a persistent, internet-reachable deployment (so a cloud-hosted agent or a Telegram bot can
reach it too), see docs/deployment.md — Docker Compose + a Cloudflare
Tunnel, with no inbound ports to open.
Connect an AI agent
Every consumer needs the bearer token from .env (API_BEARER_TOKEN) as
Authorization: Bearer <token>. Pick whichever path matches your client.
MCP clients (Claude Code, Claude Desktop, OpenWebUI, …)
Claude Code, against a running deployment:
claude mcp add --transport http aws-devops-study-kb https://<your-host>/mcp \
--header "Authorization: Bearer <API_BEARER_TOKEN>"Claude Desktop or any client configured via a mcpServers JSON block:
{
"mcpServers": {
"aws-devops-study-kb": {
"type": "http",
"url": "https://<your-host>/mcp",
"headers": { "Authorization": "Bearer <API_BEARER_TOKEN>" }
}
}
}OpenWebUI: add it as an MCP tool server (Settings → Tools) with the same URL + header, if your OpenWebUI version has native MCP support.
Once connected, the server's MCP instructions field (read automatically by clients that honor
it) already nudges the model to search before answering and save memos unprompted — see
docs/agent-persona.md if you want to make that behavior explicit and
reliable via a system prompt instead of relying on the client picking it up.
OpenAPI/REST tool-calling clients
For anything that imports an OpenAPI spec rather than speaking MCP (custom GPT Actions, LibreChat, a custom bot):
Spec URL:
https://<your-host>/openapi.json(bearer-token protected — fetch it with the header)A static copy is committed at
openapi/generated.jsonif your client needs a file instead of a live fetch (regenerate withnpm run generate:openapiafter any schema change).Every call needs the same
Authorization: Bearer <API_BEARER_TOKEN>header.
Tag convention
There are no folders — tags are the only organizing structure, so agents need to use them
consistently. exam:<domain> for the 6 AWS DevOps Pro exam domains, plus freeform topic tags
(ebs, s3, codepipeline, …). Full convention, and where it's enforced/documented for agents,
in docs/tag-conventions.md.
Development
npm test # vitest — memoService, REST API, and MCP tool round-trips
npm run typecheck # tsc --noEmit
npm run build # compiles to dist/ (used by the Docker image)See CLAUDE.md for the full architecture writeup (why SQLite+FTS5, why one shared
service layer, the auth model, known gotchas) if you're extending this.
License
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Related MCP Connectors
Shared, governed long-term memory for AI agents across tools and sessions via MCP and REST.
Cross-AI personal memory. Save once in ChatGPT, recall in Claude, Mistral, Grok, or any MCP client.
Universal memory for AI agents and tools. Save, organize and search context anywhere.
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