edit2ppt 0.5.0

AI-agent-native PPTX engine: generate, chat-edit and preview decks as a Python library, agent tool set, MCP server or hosted service. Korean-first. Forked from ppt-master (MIT).

edit2ppt

AI-agent-native PPT generation server. Korean-language-first. MCP-ready.

한국어 README · Built on ppt-master by Hugo He (MIT)

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edit2ppt is an AI-agent-native PPTX engine: generate decks from a one-line intent, chat-edit existing files slide by slide, render previews, and patch text deterministically — all producing real, natively editable PowerPoint.

Use it four ways (same engine, pick your surface):

pip install edit2ppt              # library + agent tools + local MCP
pip install "edit2ppt[server]"    # + the hosted multi-tenant service

1 · Python library

from edit2ppt import generate_pptx, edit_pptx, preview_pptx, set_pptx_text, analyze_pptx

generate_pptx("Q3 영업 결과 임원 보고", output="deck.pptx")          # intent → deck
r = edit_pptx("deck.pptx", "3번 슬라이드 제목을 'Q3 요약'으로 바꿔줘")  # one chat turn
svgs = preview_pptx(r.path)                                          # slides → SVG
info = analyze_pptx(r.path)                                          # text outline + addresses
set_pptx_text(r.path, [{"slide": 0, "shape_id": 2, "para": 0,
                        "new_text": "새 제목"}])                      # no-LLM, instant

BYOK: api_key=... or ANTHROPIC_API_KEY. preview/set_text/analyze need no key.

2 · Agent tools (function calling)

from edit2ppt.agent_tools import ANTHROPIC_TOOLS, run_tool

msg = client.messages.create(model="claude-opus-4-7", tools=ANTHROPIC_TOOLS, ...)
for block in msg.content:
    if block.type == "tool_use":
        result = run_tool(block.name, block.input)

3 · Local MCP server (zero infra)

// Claude Desktop / Claude Code / Cursor
{ "mcpServers": { "edit2ppt": {
    "command": "edit2ppt-mcp",
    "env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
} } }

Five tools over local files: generate_pptx, edit_pptx, preview_pptx, set_pptx_text, analyze_pptx. No database, no storage, no server.

4 · Hosted service

edit2ppt serve runs the FastAPI service (REST + SSE jobs + hosted MCP) that powers the web studio — multi-tenant assets, job queue, S3-compatible storage.

Two things make it different from ppt-master:

1. Agent-native at every layer. The same stateless tool functions back the library, the function-calling schemas, both MCP servers and the hosted API.
2. Korean-native. Hangul text width, Korean fonts, OOXML lang="ko-KR", bilingual error messages, and Korean layout templates ship out of the box.

Use your own PPTX as the template

Upload any .pptx via POST /v1/assets (or the MCP upload_source tool) and pass its id as template_asset_id on generate-deck:

• deck_mode: "template_restyle" — a fresh deck built inside your package: your slide masters, layouts, theme colors and fonts are preserved (background chrome / logos render behind every generated slide); the original slides are removed. The Strategist receives a deterministic analysis of your theme (colors / fonts / canvas / tone samples) and designs to match it.
• deck_mode: "template_extend" — the generated slides (native DrawingML shapes, charts, icons and SVG-derived graphics) are appended after your existing slides, so the deck grows in place.

16:9 and 4:3 templates are supported; generated coordinates are rescaled to the host deck's exact slide dimensions.

Chat-edit an existing deck

Two endpoints power the web studio's "PPT 같이 만들기" (and are exposed over MCP as edit_deck):

• POST /v1/preview — deterministic, synchronous: every slide rendered to a self-contained SVG (masters/layouts inlined, images base64-embedded) for browser display.
• POST /v1/jobs/edit-deck — one chat turn: an LLM planner turns the instruction + deck outline into slide-level operations (edit / add / delete), one LLM call per touched slide rewrites its SVG, then a deterministic recompose splices the result back into the package. Untouched slides keep their identity (ids, notes, animations). Each turn produces a new pptx asset; the prior revision is preserved. Question-only turns answer in chat without changing the deck.

Architecture (at a glance)

External Agent (Claude / Cursor / your bot)
        │ MCP (HTTP+SSE)
        ▼
┌─────────────────────────────────────────────────┐
│  edit2ppt server                                │
│   MCP routes  ─┐                                │
│   REST routes ─┤── Job queue (arq + Redis) ──┐  │
│                │                              │  │
│                ▼                              ▼  │
│        Tool functions (Python)        Workers   │
│                │                              │  │
│                ▼                              ▼  │
│   Core engine (ppt-master fork + Korean patches) │
│                                                  │
│   PostgreSQL · Object storage (S3) · Redis      │
└─────────────────────────────────────────────────┘

See ppt-master-analysis/ for the full design dossier (philosophy, pipeline, gap analysis, integration plan, roadmap, conventions).

Status

Milestone	What works	What's coming
M0	Package skeleton, i18n catalog, FastAPI scaffold, health endpoint, ASCII-paths lint	—
M1	Core engine in src/edit2ppt/core/, Chinese assets renamed to English, G1/G2/G3 Korean Critical patches + 66 unit tests pass	—
M2	Layer 2 Tool functions (convert/strategize/execute/quality/export/audio) + Anthropic SDK BYOK + 1-shot generate_deck orchestrator + 77 tests	—
M3	docker-compose.yml + Postgres/Redis/MinIO + SQLAlchemy + Alembic + S3 storage + Korean filename roundtrip + Asset/Job/SSE endpoints + 119 tests	—
M4	MCP server (stdio + HTTP+SSE + Streamable HTTP) with list_templates, list_voices, upload_source, get_asset, download_url, generate_deck (with progress notifications) + Claude Desktop / Cursor guide + 149 tests	—
M5	—	Korean prompts/templates (*.ko.md)
M6	—	Auth / multi-tenant / observability
M7	—	Korean layout templates + branding

Bilingual conventions (load-bearing)

Two tracks, strictly separated:

Track	Where	Language
A	filesystem, code identifiers, DB schema, API path, storage keys	English ASCII only
B	UI text, error messages, MCP tool descriptions	Korean + English, paired
C	user content, slide text, speaker notes, TTS	user's language, preserved

Enforced by a pre-commit ASCII lint and a unit test. See ppt-master-analysis/06-bilingual-conventions.md.

Connecting an AI agent via MCP

Once docker compose up -d is running and the dev server is up, agents (Claude Desktop, Cursor, etc.) can connect over either transport.

Local stdio — agent launches edit2ppt as a subprocess:

{
  "mcpServers": {
    "edit2ppt": {
      "command": "/path/to/edit2ppt/.venv/bin/python",
      "args": ["-m", "edit2ppt.mcp.stdio_main"]
    }
  }
}

Remote HTTP — agent calls https://your-host/mcp (Streamable HTTP) or /mcp-sse/sse (legacy SSE). See docs/mcp-clients.md for full configuration including BYOK + auth headers.

Development

Requires Python 3.11+. Use uv for fast dependency management.

# Create env + install deps
uv venv .venv
uv pip install --python .venv/bin/python -e .[dev]

# Run tests
.venv/bin/python -m pytest

# ASCII path lint
.venv/bin/python scripts/lint_ascii_paths.py

# Run dev server (once dependencies are installed)
.venv/bin/python -m edit2ppt.cli serve --reload
# → http://localhost:8000/health
# → http://localhost:8000/v1/messages/sample (with Accept-Language: ko-KR)

License

MIT. Built on top of ppt-master by Hugo He, also MIT-licensed. Attribution preserved.

Acknowledgments

• ppt-master — the SVG-to-OOXML conversion engine that powers our Layer 1.
• SVG Repo · Tabler Icons · Simple Icons · Phosphor Icons — icon libraries inherited from ppt-master.