{
  "mcpServers": {
    "dataflowr": {
      "type": "http",
      "url": "https://dataflowr.paris.inria.fr/mcp"
    }
  }
}

{
  "mcpServers": {
    "dataflowr": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--with", "dataflowr[mcp]", "python", "-m", "dataflowr.mcp_server"]
    }
  }
}

{
  "permissions": {
    "allow": ["mcp__dataflowr"]
  }
}

# with uv (recommended)
uv pip install dataflowr          # CLI only
uv pip install dataflowr[api]     # CLI + REST API
uv pip install dataflowr[mcp]     # CLI + MCP server
uv pip install dataflowr[all]     # everything

# with pip
pip install dataflowr
pip install dataflowr[mcp]

git clone https://github.com/dataflowr/dataflowr-tools
cd dataflowr-tools
uv pip install -e ".[all]"

# Course overview
dataflowr info

# List all modules
dataflowr modules list

# Filter by session, tag, or GPU requirement
dataflowr modules list --session 7
dataflowr modules list --tag attention
dataflowr modules list --gpu

# Full module details (with notebook links)
dataflowr module 12

# Fetch notebook content from GitHub
dataflowr notebook 12                        # practical (default)
dataflowr notebook 12 --kind intro
dataflowr notebook 12 --no-code              # markdown only

# Fetch course website page text (raw markdown from dataflowr/website)
dataflowr page 12

# Fetch lecture slides (from dataflowr/slides)
dataflowr slides 12

# Fetch quiz questions (from dataflowr/quiz)
dataflowr quiz 2a
dataflowr quiz 3

# Browse transcript knowledge base (318 concept notes from lectures)
dataflowr transcripts search "backprop"
dataflowr transcripts get "training loop"

# Compare catalog against website + slides repos
dataflowr sync

# Search by keyword
dataflowr search "attention transformer"
dataflowr search "generative"

# Sessions
dataflowr sessions list
dataflowr sessions get 7

# Homeworks
dataflowr homeworks list
dataflowr homeworks get 1

# JSON output (pipe-friendly)
dataflowr modules list --json | jq '.[] | select(.session == 9)'
dataflowr module 18b --json

uvicorn dataflowr.api:app --reload
# → http://localhost:8000
# → http://localhost:8000/docs  (Swagger UI)

curl http://localhost:8000/modules/12
curl http://localhost:8000/sessions/7
curl http://localhost:8000/search?q=diffusion
curl "http://localhost:8000/modules?session=9&gpu=true"
curl "http://localhost:8000/modules?tag=attention"
curl "http://localhost:8000/modules/12/notebooks/practical/content?include_code=false"
curl http://localhost:8000/modules/12/page
curl http://localhost:8000/modules/2a/quiz
curl http://localhost:8000/modules/3/quiz
curl "http://localhost:8000/transcripts/search?q=backprop"
curl http://localhost:8000/transcripts/training%20loop

python -m dataflowr.mcp_server

python -m dataflowr.mcp_server --http
# → POST http://localhost:8001/mcp  (or $PORT if set)

{
  "mcpServers": {
    "dataflowr": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "--with", "dataflowr[mcp]", "python", "-m", "dataflowr.mcp_server"]
    }
  }
}

{
  "permissions": {
    "allow": ["mcp__dataflowr"]
  }
}

claude mcp add --scope user dataflowr -- uv run --with dataflowr[mcp] python -m dataflowr.mcp_server

{
  "mcpServers": {
    "dataflowr": {
      "command": "python",
      "args": ["-m", "dataflowr.mcp_server"]
    }
  }
}

{
  "mcpServers": {
    "dataflowr": {
      "command": "uv",
      "args": ["run", "--with", "dataflowr[mcp]", "python", "-m", "dataflowr.mcp_server"]
    }
  }
}

{
  "mcpServers": {
    "dataflowr": {
      "command": "python",
      "args": ["-m", "dataflowr.mcp_server"]
    }
  }
}

{
  "servers": {
    "dataflowr": {
      "type": "stdio",
      "command": "python",
      "args": ["-m", "dataflowr.mcp_server"]
    }
  }
}

{
  "mcpServers": {
    "dataflowr": {
      "type": "http",
      "url": "http://localhost:8001/mcp"
    }
  }
}

1. search_modules "attention"      → find relevant modules by keyword
2. get_module "12"                 → full details, notebook links, prerequisites
3. get_page_content "12"           → read the lecture notes
4. get_notebook_content "12"       → work through the exercises
5. get_quiz_content "12"           → self-test your understanding
6. search_transcripts "attention"  → find concept notes from lecture transcripts
7. get_transcript_note "training loop" → read timestamped quotes and cross-references

from dataflowr import COURSE

# Get a module
module = COURSE.get_module("12")
print(module.title)          # "Attention and Transformers"
print(module.description)
print(module.notebooks)

# Search
results = COURSE.search("attention")

# Get a session
modules = COURSE.get_session_modules(7)

# Navigate the full catalog
for module in COURSE.modules.values():
    if module.requires_gpu:
        print(f"Module {module.id}: {module.title}")