# refigure [Health: Active] **Category:** 💻 Developer Tools **Repository:** https://github.com/HelgDemidov/refigure **GitHub Stars:** 0 **npm Downloads (last month):** 6 **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/refigure ## Description Convert DOCX/XLSX to Markdown with native OOXML chart-data extraction (real numbers, no OCR or rasterization) and zero-loss composite-figure markers for images a chart parser cannot read. CLI, MCP server (stdio + Streamable HTTP), optional VLM fallback for figures with no native chart data. ## Claude Desktop Quick Installation Install path detected from listing signals. Uses `uvx` (confidence: high): ```json "mcpServers": { "refigure": { "command": "uvx", "args": ["--from"] } } ``` ## Documentation & README # refigure **Converters where figures survive.** [![CI](https://github.com/HelgDemidov/refigure/actions/workflows/ci.yml/badge.svg)](https://github.com/HelgDemidov/refigure/actions/workflows/ci.yml) [![Coverage](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/HelgDemidov/refigure/main/docs/assets/coverage-badge.json)](https://github.com/HelgDemidov/refigure/actions/workflows/ci.yml) [![License: Apache 2.0](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](LICENSE) [![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](pyproject.toml) [![PyPI](https://img.shields.io/pypi/v/refigure)](https://pypi.org/project/refigure/) [![Docker](https://img.shields.io/badge/ghcr.io-refigure-2496ED?logo=docker&logoColor=white)](https://github.com/HelgDemidov/refigure/pkgs/container/refigure) [![MCP Registry](https://img.shields.io/badge/MCP_Registry-listed-6f42c1)](https://registry.modelcontextprotocol.io/?q=refigure) [![Claude Desktop](https://img.shields.io/badge/Claude_Desktop-.mcpb-D97757)](https://github.com/HelgDemidov/refigure/releases/latest/download/refigure.mcpb) [![AllMCPs Verified](https://allmcps.com/api/badge/refigure?style=shield)](https://allmcps.com/mcp/refigure) DOCX/XLSX → Markdown that keeps charts and infographics machine-readable instead of losing them to OCR or a vision model: native OOXML chart data (`numCache`/`strCache`) recovers exact numbers with zero GPU calls, zero VLM calls, zero lost precision — by default, not as a fallback. That default path is also why the base install (`pip install "refigure[docx,xlsx]"`) is **~500x lighter than PyTorch-based alternatives** (5.6MB vs. multi-GB) — the core conversion needs no ML model at all. That number is about the core architecture, not every distribution format: the Docker image trades it back deliberately, bundling VLM providers + LibreOffice for a turnkey composite-figure path (see Docker below). VLM interpretation itself is there for the rare figure with no native data at all (a dashboard screenshot) — never required just to get real numbers out of a chart, on any distribution format. Ships as a library, CLI, MCP server, and a one-click Claude Desktop bundle — every surface returns the same native-fidelity output, not a degraded summary for agents. ## Features - **Native chart-data extraction** — reads OOXML `numCache`/`strCache` directly; no rasterize/OCR/VLM step for charts, real numbers every time. - **Positioned zero-loss markers for composite figures** (DOCX) — grouped shapes/infographics that mammoth would otherwise silently fragment into disconnected pieces get a clean marker instead, with position and any caption text preserved. Absent even in well-funded incumbents — see [Docling issue #1287](https://github.com/docling-project/docling/issues/1287). - **Optional VLM interpretation** (DOCX composite figures, `[vlm]` extra, `--vlm`/`Config(use_vlm=True)`) — cloud description + a real rendered mermaid diagram (26 supported diagram types — flowcharts, pie/xy charts, sequence/state/ER diagrams, Gantt/timeline/sankey/treemap and more, see Status below) on top of the zero-loss floor, for figures with no native chart data at all (e.g. a dashboard screenshot). Provider-agnostic — OpenRouter by default, or direct OpenAI/Ollama/vLLM/LM Studio/Anthropic via `--vlm-provider` (`[vlm-direct]` extra). `--strict` upgrades one specific failure (the system `soffice`/LibreOffice binary missing) from a graceful skip to a hard error; every other VLM failure still degrades. - **Rich, typed result** — `ConversionResult` (markdown + warnings + chart/group counts + `vlm_used`), not a bare string. - **CLI included** — `refigure` console command, stdin/stdout-first, native batch mode, typed exit codes (see below). - **MCP server included** — `refigure-mcp` console command (`[mcp]` extra), stdio or Streamable HTTP, tools/resources/prompts, batch conversion with per-file isolation (see below). - **Docker image** — `ghcr.io/helgdemidov/refigure`, both console commands on `PATH`, `soffice`/LibreOffice baked in — the VLM composite-figure path works turnkey, no manual LibreOffice install. Multi-arch — `linux/amd64` + `linux/arm64`, native Apple Silicon (see below). - **`.mcpb` bundle for Claude Desktop** — one-click install, no terminal (`docx`+`xlsx` only, see below). ## Demo **Optional VLM interpretation** — for a figure with no native chart data at all (a screenshot, not an OOXML chart part) AND no matching mermaid construct either (a dense radial sunburst — nothing in the 4 original mermaid types could represent it), `--vlm` both recovers the real content and produces a genuinely renderable diagram, not just recovered text: A real docx image (a dense wireless-technology sunburst chart with no native chart data) converted by refigure.docx.convert(use_vlm=True) into a rich VLM-generated description and a real rendered mermaid mindmap diagram, laid out radially instead of the unreadable flat strip a generic flowchart construct would have produced **Native chart-data extraction** — real OOXML `numCache`, not a screenshot, not OCR: A real xlsx bar chart converted by refigure.xlsx.convert() into Markdown, shown both as the raw text an LLM reads and as the same data re-rendered as a diagram **Same extraction, from DOCX** — Word embeds native charts too, not just Excel; refigure reads the same cached OOXML data either way: A real docx pie chart from an EU labour-platform survey converted by refigure.docx.convert() into Markdown, shown both as the raw text an LLM reads (mermaid fence + data table) and as the same data re-rendered as a diagram **Composite figures** — positioned, zero-loss, even when the figure itself can't be rendered (no incumbent does this — see [Docling issue #1287](https://github.com/docling-project/docling/issues/1287), open >1 year): A real docx composite figure (a grouped diagram refigure.docx.convert() can't render) converted into a positioned zero-loss marker that keeps the figure's own caption/legend text ## Quickstart ```bash pip install "refigure[docx,xlsx]" ``` ```bash refigure report.docx # markdown to stdout ``` ```python from refigure.docx import convert result = convert("report.docx") print(result.markdown) print(f"{result.charts_found} charts, {result.groups_found} composite figures") ``` Or without a permanent install, via [uv](https://docs.astral.sh/uv/)/`uvx`: ```bash uvx --from "refigure[docx,xlsx]" refigure report.docx ``` Optional VLM interpretation, for a composite figure the chart engine can't reconstruct on its own (see Features above): ```bash pip install "refigure[docx,vlm]" export OPENROUTER_API_KEY=... # or --vlm-api-key-file/--vlm-provider refigure report.docx --vlm # needs the system soffice/LibreOffice binary too ``` ## Installation & usage One converter, four ways to run it — pick whichever fits your pipeline. Click a heading to expand it.
CLI — a console command, stdin/stdout-first, native batch mode `refigure` installs a console command — a thin wrapper over the same `convert()` used programmatically, no separate logic: ```bash refigure report.docx # markdown to stdout refigure report.docx -o report.md # markdown to a file cat report.docx | refigure --format docx # stdin, format hint required refigure reports/ -o out/ # batch: directory, walked recursively refigure a.docx b.xlsx -o out/ # batch: 2+ explicit sources ``` Batch mode (2+ sources, or a single directory) requires `-o DIR`, keeps going past a failed source by default (`--fail-fast` aborts on the first one instead), and always prints a summary (`N/M converted, K failed`) to stderr. `--json` emits the full result — markdown plus chart/group counts and warnings — instead of plain markdown. `-v`/`-q` control verbosity; `--strict` is forwarded to the same `Config.strict` the Python API uses. Exit codes: | Code | Meaning | | --- | --- | | 0 | success | | 1 | batch mode: 1+ sources failed (keep-going default) | | 2 | usage error (bad arguments/flags) | | 3 | input isn't a valid document of its format | | 4 | input isn't a valid/safe archive | | 5 | the format's extra (`[docx]`/`[xlsx]`) isn't installed | | 6 | unexpected internal error |
MCP server — for agents/IDEs that speak the protocol directly `refigure-mcp` — the same converters as an [MCP](https://modelcontextprotocol.io) server, for agents/IDEs that speak the protocol directly instead of shelling out to a CLI or importing the library. Listed on the official [MCP Registry](https://registry.modelcontextprotocol.io/?q=refigure) as `io.github.HelgDemidov/refigure`: ```bash pip install "refigure[mcp,docx,xlsx]" refigure-mcp # stdio — the MCP client launches it ``` ```json { "mcpServers": { "refigure": { "command": "refigure-mcp" } } } ``` Or point the client at `uvx` instead, with no permanent install at all: ```json { "mcpServers": { "refigure": { "command": "uvx", "args": ["--from", "refigure[mcp,docx,xlsx,vlm-direct]", "refigure-mcp"] } } } ``` `refigure[full]` is a shortcut for `refigure[mcp,docx,xlsx,vlm-direct]` — every tool, both formats, every VLM provider, one extras string. Three tools — `convert_docx`, `convert_xlsx`, and `convert_batch` (multiple files in one call: one bad file reports its own error without aborting the rest) — each registered only if its format extra is actually installed. `use_vlm`/`--vlm-provider` and friends work the same as the CLI. A result too large to inline is stored and handed back as a `refigure://conversion/{id}` resource instead of inflating the tool response. Two prompts (`ingest_for_rag`, `explain_conversion_warnings`) help a client pick the right tool/VLM settings for the job. Streamable HTTP is opt-in, for a shared/remote deployment — bearer-token auth is required, not optional: ```bash echo "sk-... = alice" > tokens.txt refigure-mcp --transport http --mcp-auth-token-file tokens.txt ``` Per-caller rate-limiting (protects the operator's own spend from a leaked/runaway token) applies automatically over HTTP, together with a fairness soft-cap once 2+ callers are configured; `refigure-mcp --help` covers every tuning flag (concurrency, timeouts, resource-store limits, batch size, VLM ceiling).
Docker — one image, CLI and MCP server both on PATH, soffice baked in One image, both surfaces — `refigure` and `refigure-mcp` are already on `PATH`, no separate CLI/MCP builds to choose between. The one thing this format buys over `pip`/`uvx` that neither can: the system `soffice`/ LibreOffice binary the VLM composite-figure path needs is baked in, not a manual install. Multi-arch manifest (`linux/amd64` + `linux/arm64`) — `docker pull` resolves the right layer automatically, including on Apple Silicon. ```bash docker pull ghcr.io/helgdemidov/refigure:latest ``` Pin an exact version instead of `:latest` for reproducibility — e.g. `:0.3.4` — see the [package page](https://github.com/HelgDemidov/refigure/pkgs/container/refigure) for available tags. The package page's **OS/Arch** tab lists `unknown/unknown` alongside the real `linux/amd64`/`linux/arm64` entries — that's a build-provenance/SBOM attestation (in-toto + SPDX metadata this image publishes for every platform), not a broken or untrusted image. GHCR's own UI doesn't label attestation manifests, a [known, widely-reported limitation](https://github.com/docker/build-push-action/issues/900) of the registry's package view, unrelated to this project. CLI, via a bind mount (the image's working directory is already `/data`): ```bash docker run --rm -v "$PWD:/data:ro" ghcr.io/helgdemidov/refigure:latest \ refigure /data/report.docx ``` MCP, stdio — the client launches the container itself: ```json { "mcpServers": { "refigure": { "command": "docker", "args": ["run", "-i", "--rm", "ghcr.io/helgdemidov/refigure:latest", "refigure-mcp"] } } } ``` MCP, Streamable HTTP — `--mcp-http-host 0.0.0.0` is required here, not optional: the default `127.0.0.1` bind is unreachable through `-p` port publishing (Docker's NAT reaches the container's external network interface, not its loopback), so the "obvious" invocation without this flag would silently never respond: ```bash echo "sk-... = alice" > tokens.txt docker run --rm -p 8000:8000 -v "$PWD/tokens.txt:/data/tokens.txt:ro" \ ghcr.io/helgdemidov/refigure:latest \ refigure-mcp --transport http --mcp-http-host 0.0.0.0 \ --mcp-auth-token-file /data/tokens.txt ```
Claude Desktop (.mcpb) — download, double-click, done The simplest install for a non-technical user: download, double-click, done — no terminal, no `pip`/`uvx`/`docker`. Covers `docx`+`xlsx` conversion only (no VLM — that needs the `[vlm]` extra, deliberately not carried by this bundle); dependencies resolve fresh from PyPI via `uv` on first launch, the same mechanism `uvx` uses under the hood, just one click instead of a config snippet. [**Download refigure.mcpb**](https://github.com/HelgDemidov/refigure/releases/latest/download/refigure.mcpb) — open it with Claude Desktop to install.
## Real examples Concentrated excerpts (≤200 lines each) of real `convert()` output on real, openly-licensed documents — the actual markdown a pipeline would ingest, not a screenshot or a cherry-picked one-liner. Each file's own header states its source, license and attribution; trimmed sections are marked inline, never fabricated to fill space. | Source | Demonstrates | Output | | --- | --- | --- | | `hackair-d7.7-pilot-evaluation.docx` | native chart extraction — real survey tables + `xychart-beta` bar charts | [examples/hackair-native-charts.md](https://github.com/HelgDemidov/refigure/blob/HEAD/examples/hackair-native-charts.md) | | `swd2018-254-marine-litter-ia-annex.docx` | honest fallback — a chart that fails render-verification degrades to a clean table, plus 2 composite-figure zero-loss markers | [examples/swd2018-combo.md](https://github.com/HelgDemidov/refigure/blob/HEAD/examples/swd2018-combo.md) | | `govtech-2025-charts.xlsx` | XLSX native charts — 3 distinct types (`xychart-beta`/`radar-beta`/`pie`) from one workbook | [examples/govtech-xlsx-charts.md](https://github.com/HelgDemidov/refigure/blob/HEAD/examples/govtech-xlsx-charts.md) | | `swd2021-396-platform-work-ia.docx` | native pie + a 23-year time series, real EU-survey labels | [examples/swd2021-pie-chart.md](https://github.com/HelgDemidov/refigure/blob/HEAD/examples/swd2021-pie-chart.md) | | `efsa-trichinella-dashboard-guide.docx` | `--vlm` interpretation — 2 screenshot figures recovered as a bar chart and a UI flowchart, real numbers | [examples/efsa-trichinella-vlm.md](https://github.com/HelgDemidov/refigure/blob/HEAD/examples/efsa-trichinella-vlm.md) | Open any of these on GitHub and both views are right there: the raw ```` ```mermaid ```` fence an LLM/RAG pipeline would read, and its native GitHub rendering — no extra step, that's GitHub's own Markdown support. ## Status - **Validated** against 27 real documents (15 DOCX + 12 XLSX) — 407 native charts found (400 rendered), 35 composite figures recovered as positioned zero-loss markers. Full provenance: [`tests/integration/fixtures/manifest.yaml`](tests/integration/fixtures/manifest.yaml). - **Tested**: CI gates on a combined unit+integration coverage floor of 95%. - **Published** as `v0.3.4` — [PyPI](https://pypi.org/project/refigure/) (trusted publishing, no stored tokens), [GHCR](https://github.com/HelgDemidov/refigure/pkgs/container/refigure), and the official [MCP Registry](https://registry.modelcontextprotocol.io/?q=refigure) as `io.github.HelgDemidov/refigure`. `refigure-md` is a reserved alternate name, not an active release. Extracted from a working document-analysis pipeline (a government AI-policy research corpus), not built from scratch for this release. VLM interpretation of composite figures the chart engine can't reconstruct is fully implemented and tested, not a stub — `[vlm]` extra, provider-agnostic (direct OpenAI/Anthropic via `[vlm-direct]`), also needs the system `soffice`/LibreOffice binary. Mermaid-diagram recognition depends on diagram type and on what the source figure actually contains: - Common types (flowcharts, pie/xy charts) are picked reliably. - More specialized ones need an unambiguous visual cue on the source figure. - Not every figure produces a diagram — a plain-text description is an honest fallback, not a failure. **PDF is out of scope, on purpose — a boundary, not a gap.** PDF has no equivalent of OOXML's cached chart data (`numCache`/`strCache`) for any mainstream chart generator, so the native, rasterize-free extraction this project is built on doesn't transfer to it — confirmed by research into PDF's own structure and how leading PDF converters handle charts today, not assumed. For mixed-format corpora, route by extension instead of expecting one tool to cover everything: ```python import refigure.docx import refigure.xlsx if path.suffix == ".pdf": markdown = docling_convert(path) # or any PDF-capable converter elif path.suffix == ".docx": markdown = refigure.docx.convert(path).markdown else: markdown = refigure.xlsx.convert(path).markdown ``` Use [Docling](https://github.com/docling-project/docling) or [MarkItDown](https://github.com/microsoft/markitdown) for PDF, refigure for DOCX/XLSX where the chart data actually survives in the file. ## License Apache-2.0 — see [LICENSE](LICENSE) and [NOTICE](NOTICE).