Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Query Moodle LMS data through natural language. Attach your agent to your LMS data.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
MOODLE_URL, MOODLE_TOKEN비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"moodle-aql": {
"command": "npx",
"args": [
"-y",
"moodle-mcp-server-aql"
],
"env": {
"MOODLE_URL": "your-value",
"MOODLE_TOKEN": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add moodle-aql --env MOODLE_URL=your-value --env MOODLE_TOKEN=your-value -- npx -y moodle-mcp-server-aql실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"moodle-aql": {
"command": "npx",
"args": [
"-y",
"moodle-mcp-server-aql"
],
"env": {
"MOODLE_URL": "your-value",
"MOODLE_TOKEN": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"moodle-aql": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"moodle-mcp-server-aql"
],
"env": {
"MOODLE_URL": "your-value",
"MOODLE_TOKEN": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 csmediapro 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: MOODLE_URL, MOODLE_TOKEN
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.1.6)은 42일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Signoff
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Five gates that make an agent's 'done' provable: run them, read the verdict, audit the record.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Ask your Moodle instance anything. Get structured answers in seconds.
moodle-mcp-server is an open-source MCP (Model Context Protocol) server that connects AI agents directly to Moodle's Web Services API. It is the first reference implementation of the Agentic Query Layer (AQL) pattern — a software design pattern for giving large language models safe, permission-aware, read-only access to structured data.
Instead of learning report builders, writing SQL, or exporting CSVs, you ask questions in plain English — the AI agent queries your LMS and returns structured data.
AQL pattern: https://csmediapro.com/notes/agentic-query-layer Project home: https://csmediapro.com/products/moodle-mcp-server npm package: https://www.npmjs.com/package/moodle-mcp-server-aql MCP Registry: io.github.csmediapro/moodle-mcp-server-aql
Most users launch the server through an MCP client such as Claude Desktop:
{
"mcpServers": { "moodle-mcp-server-aql": { "command": "npx", "args": ["-y", "moodle-mcp-server-aql"], "env": { "MOODLE_URL": "https://your-moodle-instance.example", "MOODLE_TOKEN": "your-moodle-web-services-token" } } } }
# Clone the repo
git clone https://github.com/csmediapro/moodle-mcp-server cd moodle-mcp-server
npm install
cp packages/server/.env.example packages/server/.env
npm run server:build node packages/server/dist/index.js
User field display settings are generated per Moodle instance and stored locally at packages/server/data/user-field-schema.json. This file is intentionally ignored by git because it can include site-specific custom profile fields.
After connecting to a Moodle site, run the refresh_user_field_schema tool once to discover available standard and custom user fields. A minimal example shape is included at packages/server/data/user-field-schema.example.json.
Separately installed plugins can use this schema to flatten custom profile fields into stable keys such as school. The premium user-directory plugin, for example, uses it to cache and summarize full-user directory fields outside the OSS core.
The core owns two different server identity fields:
server.id — stable machine identity, for example mcp_8f3k2q9xserver.name — human-facing display labelIf server.id is missing, the core generates one once and persists it to the resolved config file before startup continues.
Environment overrides:
MOODLE_MCP_CONFIG or MOODLE_MCP_SERVER_CONFIG — choose the config file pathSERVER_ID — explicit server.id overrideSERVER_NAME — explicit server.name overrideSERVER_VERSION — explicit server.version overrideIf server.id is missing and the resolved config path is not writable, startup fails deliberately.
# From the project root
cp packages/client/.env.example packages/client/.env npm run client:dev
The client will auto-detect your Moodle instance and present a chat interface where you can ask questions in plain English.
moodle-mcp-server needs an AI model to power the natural-language interface. You bring the model — the moodle-mcp-server core and reference client support any MCP-compatible provider.
Running a local model keeps all data on your own hardware — nothing leaves your network. Modern quantized models run well on consumer GPUs and even CPU-only setups.
Performance: A quantized 24B model on a single RTX 3090 delivers ~1.5-second responses after the first query — faster than most cloud APIs once the system is initialized.
# Install Ollama: https://ollama.com
ollama pull gemma3:12b # Fast, reliable tool use (~200ms TTFT) ollama pull qwen3:14b # Strong reasoning, good for complex queries ollama pull deepseek-r1:14b # Excellent at multi-step chains
Then point the reference client at http://localhost:11434 (Ollama's default).
# Download a GGUF model (example: Devstral 24B Q4)
llama-server -m devstral-24b-Q4_K_M.gguf --ctx-size 60000 --port 8080
Point the reference client at http://localhost:8080/v1.
| Model | Size | Best For | Hardware |
|---|---|---|---|
| Gemma 3 12B | ~7 GB VRAM | Fast tool calls, straightforward queries | Single consumer GPU |
| Qwen 3 14B | ~8.5 GB VRAM | Complex reasoning, multi-tool chains | Single consumer GPU |
| Devstral 24B Q4 | ~14.5 GB VRAM | Maximum capability, 60K context | RTX 3090 / 4090 |
Anthropic (Claude):
export ANTHROPIC_API_KEY=sk-ant-...
Select "Anthropic" in the reference client's provider dropdown. Claude Sonnet offers the most reliable tool-calling behavior.
OpenAI (GPT):
export OPENAI_API_KEY=sk-...
Select "OpenAI" in the provider dropdown. GPT-4o performs well on structured queries.
Ollama Cloud: Uses the same API as local Ollama, hosted at https://ollama.com/v1. Good middle ground — faster than local cold starts, more private than big cloud providers.
Claude Desktop connects to the moodle-mcp-server core directly over stdio — no reference client needed.
Add to your Claude Desktop config (claude_desktop_config.json):
{
"mcpServers": { "moodle-mcp-server": { "command": "node", "args": ["/path/to/moodle-mcp-server/packages/server/dist/index.js"], "env": { "MOODLE_URL": "https://your-moodle-instance.com", "MOODLE_TOKEN": "your-api-token" } } } }
Restart Claude Desktop. The server's tools will appear in Claude's tool list — ask questions directly.
| Tool | Description |
|---|---|
list_courses | Full course catalog with category drill-down |
get_course | Detail view for any course |
list_course_users | Enrolled users with roles and access data (now supports course name search with interactive selection) |
list_assignments | All assignments with due dates |
list_categories | Full hierarchy with exact parent resolution |
get_site_info | Instance overview — site name, version, course count |
get_user | Detail view for a Moodle user |
list_user_courses | Courses for a specific user |
search_users | User search by standard Moodle identity fields |
search_courses_by_name | Search for courses by name with partial matching and interactive selection |
This project is a reference implementation of the Agentic Query Layer (AQL) pattern — a layer that sits between an AI agent and your data, providing permission-aware, read-only, plain-language access without exposing the underlying data to the model.
User (plain English question)
│ ▼ AI Agent (Claude / GPT / Gemini / Ollama / local) │ ▼ MCP Protocol moodle-mcp-server ← AQL connector for Moodle ├── Tool Registry (core + plugins) ├── Agent Runtime Config (core + plugin rules) ├── Optimized Data Layer (secure, efficient data handling) └── Moodle Client (REST API calls) │ ▼ Moodle Web Services API
dist/index.js — runs as a subprocess, used by Claude Desktop and similar clientsThe OSS core intentionally ships with stdio only. Any network-facing wrapper, remote supervision, or premium plugin attachment belongs in a separate commercial node agent or wrapper.
AGPL v3 — see LICENSE.
This means you can:
moodle-mcp-server core for free, in any environmentYou cannot:
moodle-mcp-server core as a closed-source competing commercial productMoodle is a trademark of Moodle Pty Ltd. moodle-mcp-server is an independent CSMediaPro project and is not affiliated with, endorsed by, sponsored by, or officially connected to Moodle Pty Ltd or the Moodle project. The name is used descriptively to identify compatibility with Moodle LMS.
moodle-mcp-server is built and maintained by CSMediaPro, a software development company specializing in AI integration, systems engineering, and workflow automation. It is the first reference implementation of the Agentic Query Layer (AQL) pattern.