flint-slating 0.1.4

MCP server that reads PDFs (metadata, TOC, Markdown, text, images, tables) for downstream LLM consumers.

flint-slating

MCP server that reads PDFs and exposes them to LLM consumers as structured Markdown, plus the usual ancillaries: metadata, outline, images, tables.

Designed to pair with a separate "wiki" MCP server that handles the writing side — an agent calls flint-slating to read PDFs and another MCP to persist notes about them into a frontmattered-markdown knowledge base.

What it does

Built on a permissive-license PDF stack:

Library	License	Role
Docling	MIT	PDF → Markdown with heading hierarchy, multi-column reading order, and Markdown tables
pypdf	BSD-3	metadata, TOC, page count, encryption checks, image enumeration
pdfplumber	MIT	per-page table extraction

There is no PyMuPDF, no MuPDF, no AGPL or GPL anywhere in the dependency tree. A CI license-check job rejects PRs that pull in copyleft transitive deps.

Transports

Two transports off the same MCP server, selected via --transport:

Transport	Run via	Use case
Streamable-HTTP (default)	uvx flint-slating or --transport http	Long-lived local daemon, container, or shared service.
stdio	uvx flint-slating --transport stdio	The standard MCP integration shape — drop into claude_desktop_config.json or any mcp.json.

Run

As an HTTP daemon (default)

uvx flint-slating                    # listens on PORT (default 35833)
curl http://127.0.0.1:35833/health

Or pin it:

uv tool install flint-slating
flint-slating

As a stdio MCP server

uvx flint-slating --transport stdio

Wire into Claude Code's MCP config:

{
  "mcpServers": {
    "flint-slating": {
      "command": "uvx",
      "args": ["flint-slating", "--transport", "stdio"]
    }
  }
}

Docker

docker run --rm \
  -p 35833:35833 \
  -v $(pwd)/pdfs:/pdfs:ro \
  -v flint-slating-data:/data \
  ghcr.io/parkviewlab/flint-slating:latest

Or use docker-compose.yml for a persistent stack.

MCP tools

All PDF tools take a source argument with one of:

• {"path": "/abs/path/to/file.pdf"} — local file
• {"url": "https://..."} — streamed to a content-addressed cache
• {"bytes_b64": "...", "filename": "x.pdf"} — base64 upload (size-capped)

Tool	What it does
pdf_info	{page_count, metadata, is_encrypted, sha256}
pdf_toc	flat outline [{level, title, page}]
pdf_read_text	plain text by page range (fast — pypdf, no ML)
pdf_read_markdown	high-quality Markdown via Docling (hybrid sync/async — see below)
pdf_read_chunks	per-page Markdown chunks with tables/images/toc_items (hybrid sync/async)
pdf_list_images	enumerate images: [{page, index, name, width, height, ext}]
pdf_extract_image	base64 bytes of one image
pdf_find_tables	per-page Markdown tables via pdfplumber
get_job_status	poll a background job
get_job_result	fetch a finished job's artifact
cancel_job	cancel a running job

Hybrid sync/async

pdf_read_markdown and pdf_read_chunks run inline when page_count <= SYNC_PAGE_THRESHOLD (default 20). For larger PDFs they queue a background job and return a job_id — poll get_job_status until state=="done", then call get_job_result (or, in HTTP mode, fetch output_url directly).

stdio mode transparently waits for the job inline — there's no HTTP server to download from, so the originating tool call blocks until the result is ready and returns it directly.

HTTP endpoints (HTTP mode only)

• GET /health — {ok, version, uptime_seconds}
• GET /admin/version — package and dependency versions, Docling model status
• GET /admin/jobs — recent job list
• GET /outputs/{job_id}/result.md — finished Markdown
• GET /outputs/{job_id}/result.json — finished chunked output
• GET /outputs/{job_id}/log.jsonl — append-only job log
• POST /sse — MCP Streamable-HTTP transport

Configuration

Env var	Default (daemon)	Default (container)	Purpose
PORT	35833	35833	HTTP bind port
HOST	0.0.0.0	0.0.0.0	HTTP bind address
OUTPUT_ROOT	./output	/data/output	Per-job output dirs
CACHE_ROOT	./cache	/data/cache	Materialized URL / base64 PDFs
OUTPUT_EXPIRY_DAYS	7	7	Sweep finished jobs older than N days
MAX_INLINE_PDF_BYTES	25 MB	25 MB	Cap on base64 upload size
MAX_URL_PDF_BYTES	200 MB	200 MB	Cap on URL download size
SYNC_PAGE_THRESHOLD	20	20	Inline-vs-job cutoff for Markdown conversion
DOCLING_ARTIFACTS_PATH	~/.cache/docling	/opt/docling-models	Docling layout-model cache
ENABLE_OCR	false	false	Enable Docling OCR (Tesseract required)
PUBLIC_BASE_URL	http://localhost:35833	http://localhost:35833	Used to build output_url

Resource notes

• Docling downloads a ~200–500 MB layout model on first use. The container image does not pre-fetch it (pre-fetching dominated multi-arch build time under QEMU); the daemon warms it on startup, and the first user-facing call pays the download. Operators can populate DOCLING_ARTIFACTS_PATH (default /opt/docling-models in the container) via volume mount for a hot start.
• pypdf, pdfplumber, and the URL / base64 paths are fast and have no ML overhead — use pdf_info, pdf_toc, pdf_read_text, and pdf_find_tables whenever Markdown isn't strictly needed.

Releasing

Tag-driven CI publishes to both PyPI (flint-slating) and GHCR (ghcr.io/parkviewlab/flint-slating):

# Bump version in pyproject.toml first, then:
git tag v0.1.0
git push origin v0.1.0

The release workflow refuses tags that don't match pyproject.toml's version, or that aren't on origin/main.

License

MIT. flint-slating only depends on permissive-licensed libraries; the CI license-check job enforces this on every PR.

torch and torchvision are pinned to the CPU-only PyTorch wheel index so the distribution does not bundle NVIDIA's proprietary CUDA libraries. Inference runs on CPU on Linux/Windows and on MPS (Metal) on Apple Silicon. See THIRD_PARTY_LICENSES.md for the per-dependency license breakdown.