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pdf-mcp

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pdf-mcp

PyPI version Python 3.10+ License: MIT GitHub Issues CI codecov Downloads

Agentic RAG over your PDFs, one file or a whole folder, as a single MCP tool.

The agent decides when to search; pdf-mcp does the retrieval and hands back excerpts. It is an MCP server that lets Claude Code and other AI agents search one PDF or a whole folder by meaning or keyword, read only the pages that matter, and cleanly pull out tables, images, and scanned text, even from multi-column and Japanese layouts, with optional CUDA acceleration for warming large corpora.

mcp-name: io.github.jztan/pdf-mcp

Try it in your browser

See what your AI agent sees →

Drop in any PDF, or a whole folder of them, and watch an agent triage the corpus, search across every document at once, and read only the pages that matter, using a fraction of the tokens. 100% client-side, no install required.

Why pdf-mcp?

Without pdf-mcp

With pdf-mcp

Large PDFs

Context overflow

Read only the pages you need

Finding content

Load everything

Hybrid search: BM25 keyword + semantic

Folders of PDFs

One document at a time

Warm, triage, and search a whole folder

Warming a big folder

Minutes of CPU embedding

Length-sorted small-batch CPU encode; optional CUDA embedding, one to two orders of magnitude faster on an NVIDIA card

Tables and charts

Lost in raw text

Structured rows, and (x, y) data from vector charts

Multi-column and vertical layouts

Columns interleaved

Correct reading order, including Japanese tategaki

Scanned PDFs

No text at all

OCR via Tesseract, parallel across pages

Repeated access

Re-parse every time

SQLite cache that survives restarts

Hidden or injected text

Silently ingested

Flagged as untrusted, nothing stripped

Installation

pip install pdf-mcp

That is the whole install: hybrid search, corpus tools, multi-column and CJK reading order all work out of the box.

OCR on scanned PDFs additionally needs system Tesseract:

brew install tesseract        # macOS
apt install tesseract-ocr     # Ubuntu/Debian
winget install Tesseract-OCR  # Windows

GPU embedding is optional and off by default. On an NVIDIA card it makes the embedding pass one to two orders of magnitude faster; set PDF_MCP_CUDA=1 after installing the CUDA build of onnxruntime. Setup per CUDA series is in docs/configuration.md.

Quick Start

claude mcp add pdf-mcp -- pdf-mcp

Then ask Claude to read a PDF. For Claude Desktop, VS Code, Codex CLI, Kiro, or any other MCP client, see docs/clients.md.

pdf-mcp's tools are also plain Python functions, so you can import them and hand a PDF to the Anthropic SDK without running a server. Two runnable scripts, for a question and for a whole document: examples/.

Why this exists, and what broke along the way: Claude's 100-page PDF limit and how I got around it

Tools

13 specialized tools rather than one monolithic one. Typical pattern: pdf_info to plan, pdf_search to locate (its paragraph excerpts often answer the question outright), pdf_read_pages when you need more. For a folder, pdf_corpus_overview to triage, then pdf_corpus_search.

Tool

What it does

pdf_info

Page count, metadata, TOC summary, scanned-page detection. Call first.

pdf_search

Hybrid search (keyword + semantic), page or section granularity, paragraph or context-window excerpts with source coordinates

pdf_read_pages

Read specific pages or ranges, with OCR on demand, tables, and embedded images

pdf_read_all

Read a whole document in one call, byte-capped

pdf_get_toc

Full table of contents for documents with many bookmarks

pdf_render_pages

Render pages as PNG for vision models: diagrams, handwriting, scans

pdf_extract_chart

Chart data as exact (x, y) tables, read from plot geometry

pdf_corpus_warm

Warm a folder of PDFs into the cache within a time budget

pdf_corpus_overview

Per-document triage cards for a folder

pdf_corpus_search

Search across a folder, with document and page provenance; excerpt_style="auto" picks the excerpt unit per query

pdf_cache_stats

Per-document cache breakdown and total size

pdf_cache_clear

Clear expired or all cache entries

server_info

Which optional features and config are active

Text returned by any of these is untrusted content extracted from a PDF. pdf_info(content_trust=True) reports hidden text a human reader cannot see, and the read tools flag it per page.

Example prompts:

"Read the PDF at /path/to/document.pdf"
"Which pages discuss supply chain risks?"
"Find sections about the training process"
"Show me what page 5 looks like"
"OCR pages 3-5 of the scanned PDF"

Full reference, every parameter and response shape: docs/tool-reference.md. Embedding model selection: docs/embedding-models.md.

Example Workflow

For a large document (e.g., a 200-page annual report):

User: "Summarize the risk factors in this annual report"

Agent workflow:
1. pdf_info("report.pdf")
   → 200 pages, TOC shows "Risk Factors" on page 89

2. pdf_search("report.pdf", "risk factors")
   → Matches with structural paragraph excerpts: each excerpt
     is the bullet, paragraph, or heading that matched, not a
     fixed-width window. Often enough to answer directly.

3. If excerpts are sufficient → synthesize answer

4. If more context needed:
   pdf_read_pages("report.pdf", "89-95")
   → Full page text for deeper reading

Remote / HTTP transport

STDIO is the default and is what every example above uses. pdf-mcp-http serves the same tools over HTTP, for clients that cannot spawn a process (the Anthropic API MCP connector, claude.ai custom connectors) and for a warm corpus shared by several clients.

export PDF_MCP_AUTH_TOKEN="$(openssl rand -hex 32)"
pdf-mcp-http

Paths resolve on the server, so an HTTP agent reads what is already there: files under an allow-listed root, or a URL the server fetches. It cannot hand over a file from its own machine. It is single-tenant and fails closed: with no auth token and no [paths] allow list, the process exits rather than serving an open endpoint.

Docker images are published to GHCR for amd64 and arm64, with everything baked in, so every tool works on the first request:

./deploy.sh              # token, image, start, health-check
cp your.pdf documents/   # this folder is the server's /data/pdfs

Read docs/remote-access.md for the trust boundary and threat model before deploying, and docs/configuration.md for setup, client config, and token rotation.

Configuration

pdf-mcp works out of the box. To restrict which paths and URL hosts the server may touch, tune cache and worker settings, or add your own content-trust phrases, see docs/configuration.md.

Roadmap

See ROADMAP.md for planned features and release history.

Contributing

Contributions are welcome. See docs/contributing.md for setup, checks, the coherence eval harness, and quality-loop guidelines.

Contributors

Thank you to everyone who has helped improve this project through code, reviews, testing, and feature requests:

@Summer907 · @ebbsanchez · @VooDisss · @DerDennisOP · @deepdmk · @TheSOV

Per-release contributor credits are listed in the Changelog.

Security

Found a vulnerability? See SECURITY.md for the threat model, reporting channel, and expected response timeline. Please do not open a public GitHub issue for unpatched security reports.

License

MIT. See LICENSE.

Links

Blog posts

The story behind the releases. Building pdf-mcp keeps surprising me: benchmarks that go the wrong way, formats that break everything, features I had to remove. I write about that thinking in The Dispatch. Come along if that's your kind of thing.

Background, benchmarks, and design notes from building pdf-mcp:

Getting started

Corpus & multi-document search

Search & retrieval

Engineering & security