pagecite

Summaries of research PDFs you can actually trust: every claim cites its page, and every quote and number is checked against the PDF before you see it.

Point it at a paper, an analyst report or a pitch deck (a file, a folder or a URL). Claude writes the TL;DR, key arguments, data points, quotes worth keeping and open questions. Then pagecite checks each one against the PDF's own text, with no model involved: is the quote really on the cited page? Do the numbers appear there? Anything that fails is kept but marked ⚠ not verified, with the reason, so a wrong page or a mangled figure never passes as fact.

_{pagecite demo: a fictional report and a sample summary bundled with pagecite (no API call), with two claims deliberately wrong. The checks are the real ones.}

Why

AI summaries read well and are hard to audit. The usual failure isn't a summary that's obviously wrong. It's a plausible sentence with a number that drifted ("27%" where the report says 22%), or a quote that's real but from a different section than the claim it supports. You only catch those by rereading the document, which is what the summary was supposed to save you.

pagecite splits the work:

What it does

• Any PDF source: a file, every PDF in a folder, or an http(s) URL.
• Checked summary: TL;DR, key arguments, data points, quotes worth keeping, open questions and tags, each with page numbers. Quotes and figures are verified; open questions and the TL;DR show their pages but have nothing quotable to check.
• Obsidian notes: -o ~/Vault/Papers writes each summary as a Markdown note with YAML frontmatter (source, pages, model, verified/flagged counts, tags) and the flags inline.
• A library: every PDF you read is stored with its page text, keyed by its SHA-256. Summarising the same file again (even under another name) costs nothing. pagecite search runs full-text search over every page you've read.
• Ask with citations: pagecite ask "what drives the cost gap?" --doc 3 answers from the document with citations returned by the API itself, so every sentence points to a page. For one document all its pages are sent with a cache breakpoint, so follow-up questions read them from the prompt cache. Without --doc, the best-matching pages across your library are sent.
• MCP server: Claude Desktop, Claude Code or any MCP client can summarise PDFs, search your library, read pages and ask cited questions.

Quickstart

pipx install pagecite
pagecite demo                  # a checked summary on a fictional report, no key needed

With your own documents:

mkdir -p ~/.pagecite && echo 'ANTHROPIC_API_KEY=sk-ant-...' >> ~/.pagecite/.env   # or `ant auth login`

pagecite summarize paper.pdf
pagecite summarize ~/Downloads/reports -o ~/Obsidian/Papers   # a whole folder, as notes
pagecite summarize https://arxiv.org/pdf/1706.03762
pagecite ask "what did they measure, and on what data?" --doc 1

What counts as verified

verify.py is short and has no dependencies, so the rules are easy to check:

• Quotes must appear on the page(s) they cite. Matching ignores case, punctuation, spacing, ligatures (fi) and words split across lines (con-/straint), but keeps decimal points, so "4.2" never matches "42". An ellipsis splits a quote into parts that must appear in order. Quotes under four words are flagged as too short to prove anything.
• Numbers in a data point's value must all appear on the cited page: "USD 115 per kWh, down 14%" needs both 115 and 14 there. Thousands separators are normalised (3,200 = 3200 = 3 200).
• Pages must exist in the document.
• When a quote is on a different page, the flag says where it really is. pagecite never moves the citation for you.

What it can't check: whether a sentence in Claude's own words fairly represents its quote. That's why each claim shows the quote right under it.

Scanned PDFs without a text layer are refused (OCR them first). Documents too long for one pass are refused rather than silently truncated.

What leaves your machine

Your library is one SQLite file, ~/.pagecite/library.db.

MCP

Add to Claude Desktop's claude_desktop_config.json (or claude mcp add pagecite -- pagecite mcp in Claude Code):

{
  "mcpServers": {
    "pagecite": { "command": "pagecite", "args": ["mcp"] }
  }
}

Install with pipx install 'pagecite[mcp]'. Tools: summarize_pdf (returns each claim with its check result), ask_library, search, read (page text) and documents. search, read and documents need no API key.

Configuration

In ~/.pagecite/.env or the environment (see .env.example):

How it works

flowchart LR
  In[file · folder · URL] --> PDF[pdf.py<br/>page texts + SHA-256]
  PDF --> Lib[(library.db<br/>pages, FTS5)]
  Lib -->|new file| Sum[summarize.py<br/>Claude: structured summary]
  Sum --> Lib
  Lib --> Verify[verify.py<br/>quotes + numbers vs page text]
  Verify --> Out[terminal · Obsidian note · MCP]
  Lib --> Ask[ask.py<br/>search-result blocks, cited answers]

Verification runs every time a summary is shown, against the stored page text, so a stored summary never carries a stale result.

Development

git clone https://github.com/owen-alderson/pagecite && cd pagecite
python -m venv .venv && . .venv/bin/activate
pip install -e '.[dev]'
pytest            # no network: Claude and HTTP are faked, test PDFs are generated

Releases: push a vX.Y.Z tag and GitHub Actions publishes to PyPI. Re-record the demo with vhs docs/demo.tape.

The original Streamlit app (pdf-research-summarizer) is kept at tag v1-streamlit.

License

MIT