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:

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.

Related MCP server: MCP PDF Reader

Transports

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

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)

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

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.

Commit message convention

After the publish jobs, a changelog job generates the new CHANGELOG.md section — an LLM-written "Highlights" paragraph plus a git-cliff categorized commit list — commits it back to main, and creates the GitHub Release with the same content as its body. Categorization uses Conventional Commits prefixes (see cliff.toml):

Squash-merge PRs use the PR title as the commit subject — so the PR title is what needs the prefix. Commits without a recognised prefix are silently dropped from the CHANGELOG (still in git history). The "Highlights" paragraph requires the ANTHROPIC_API_KEY org-level secret; if the LLM call fails, a placeholder lands and the release still ships.

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0), or

• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option. In SPDX terms: MIT OR Apache-2.0.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this work by you shall be dual-licensed as above, without any additional terms or conditions. See LICENSING.md.

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.

© 2026 Gary Frattarola · Licensed under MIT OR Apache-2.0 · part of ParkviewLab