Pdf Mcp

What is Pdf Mcp about?

Pdf Mcp provides a Model Context Protocol (MCP) server that lets AI agents interact with PDF documents efficiently. It avoids loading entire files into the model context by retrieving only the relevant pages or elements.

How to use Pdf Mcp?

1. Install the package:

   pip install pdf-mcp
   # optional extras
   pip install "pdf-mcp[semantic]" "pdf-mcp[multicolumn]" "pdf-mcp[cjk]"
   

2. Start the server (default command is pdf-mcp).
3. Configure your MCP client (Claude Code, Claude Desktop, VS Code, Codex CLI, Kiro, etc.) by adding a server entry pointing to the pdf-mcp executable.
4. Call tools such as pdf_info, pdf_search, pdf_read_pages, pdf_render_pages, etc., from the client to let the agent interact with the PDF.

Key features of Pdf Mcp

• Hybrid search (BM25 keyword + semantic embeddings via Reciprocal Rank Fusion).
• Paginated reading: fetch only needed pages or sections.
• OCR support for scanned PDFs using Tesseract.
• Structured extraction of tables and embedded images.
• Column‑aware and vertical‑script (Japanese/Chinese) reading order reconstruction.
• Persistent SQLite cache with automatic invalidation on file changes.
• Secure URL fetching with HTTPS‑only and SSRF protection.
• Nine specialized MCP tools covering info, TOC, reading, rendering, searching, caching, and server diagnostics.

Use cases of Pdf Mcp

• RAG workflows where large reports, contracts, or manuals need to be queried by an LLM without hitting token limits.
• Financial analysis of multi‑page statements, extracting tables and key figures.
• Legal document review with precise page‑level retrieval and OCR of scanned contracts.
• Technical documentation assistants that can jump to relevant sections, diagrams, or code snippets.
• Multilingual research on CJK PDFs where correct reading order and semantic search are required.

FAQ

• Do I need Tesseract installed? Yes, for OCR on scanned PDFs. Install via package manager (brew, apt, or Windows installer).
• How is the cache stored? In ~/.cache/pdf-mcp/cache.db (SQLite). It caches metadata, page text, images, tables, embeddings, and rendered PNGs.
• Can I limit the size of responses? Yes, via PDF_MCP_MAX_WORKERS and response‑size limits in config.toml ([limits] max_response_bytes).
• Is semantic search optional? Yes. Install the semantic extra to pull in fastembed and related models.
• How do I clear the cache? Use the pdf_cache_clear tool.
• What clients are supported? Any MCP‑compatible client: Claude Code, Claude Desktop, VS Code Copilot, Codex CLI, Kiro, and custom clients using the standard MCP JSON format.

pdf-mcp

Surgical PDF access for AI agents — search, read, and extract without flooding context.

An MCP server that lets Claude Code and other AI agents search a PDF 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/Chinese layouts.

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

Try it in your browser

See what your AI agent sees →

Drop in any PDF and watch an agent skim it, search it, 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	Chunked reading
Token budgeting	Guess and overflow	Estimated tokens before reading
Finding content	Load everything	Hybrid search (BM25 keyword + semantic)
Tables	Lost in raw text	Extracted and inlined per page
Multi-column PDFs	Columns interleaved in extracted text	Column-aware reading order (pdf-mcp[multicolumn])
Vertical scripts (Japanese/Chinese)	Columns scrambled / glyph soup	Geometric reorder of vertical text (tategaki / 直排) — search CJK with mode='semantic' (pip install 'pdf-mcp[cjk]')
Images	Ignored	Extracted as PNG files
Repeated access	Re-parse every time	SQLite cache
Scanned PDFs	No text extracted	OCR via Tesseract (pdf_read_pages(ocr=True))
Visual content	Must describe in words	Render page as image (pdf_render_pages)
Tool design	Single monolithic tool	9 specialized tools

Features

• Hybrid search — find relevant pages with a question, not a page range. Combines BM25 keyword and semantic search via Reciprocal Rank Fusion
• Paginated reading — fetch only the pages your agent needs; large documents don't blow your context window
• OCR — scanned and image-based PDFs are fully readable and searchable via Tesseract
• Structured extraction — tables, embedded images, and table of contents returned as structured data, not text soup
• Vertical-script reading order — Japanese/Chinese tategaki (直排) reconstructed from glyph geometry into correct top-to-bottom, right-to-left order; article segmentation for dense magazine layouts; mojibake filtered
• Persistent cache — SQLite-backed; re-reads are instant and survive server restarts
• Secure URL fetching — HTTPS-only with SSRF protection; local network ranges are blocked

Contents

• Installation
• Quick Start
• Tools
• Example Workflow
• Configuration
• Roadmap
• Contributing
• Security
• License

Installation

pip install pdf-mcp

For semantic search (adds fastembed and numpy, ~67 MB model download on first use):

pip install 'pdf-mcp[semantic]'

For correct reading order on multi-column PDFs (adds pymupdf4llm, which pulls pymupdf_layout/onnxruntime):

pip install 'pdf-mcp[multicolumn]'

Without it, multi-column pages fall back to positional-sort extraction, which can interleave columns.

For Japanese/Chinese/Korean PDFs (recommended — CJK search needs semantic; extraction works without it):

pip install 'pdf-mcp[cjk]'

For OCR on scanned PDFs (requires system Tesseract):

# macOS
brew install tesseract

# Ubuntu/Debian
apt install tesseract-ocr

# Windows — download the installer from:
# https://github.com/UB-Mannheim/tesseract/wiki
# Then add the install directory to your PATH.

Quick Start

Choose your MCP client below to get started:

claude mcp add pdf-mcp -- pdf-mcp

Or add to ~/.claude.json:

{
  "mcpServers": {
    "pdf-mcp": {
      "command": "pdf-mcp"
    }
  }
}

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "pdf-mcp": {
      "command": "pdf-mcp"
    }
  }
}

Config file location:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Restart Claude Desktop after updating the config.

Requires VS Code 1.101+ with GitHub Copilot.

CLI:

code --add-mcp '{"name":"pdf-mcp","command":"pdf-mcp"}'

Command Palette:

1. Open Command Palette (Cmd/Ctrl+Shift+P)
2. Run MCP: Open User Configuration (global) or MCP: Open Workspace Folder Configuration (project-specific)
3. Add the configuration:

   {
     "servers": {
       "pdf-mcp": {
         "command": "pdf-mcp"
       }
     }
   }
   

4. Save. VS Code will automatically load the server.

Manual: Create .vscode/mcp.json in your workspace:

{
  "servers": {
    "pdf-mcp": {
      "command": "pdf-mcp"
    }
  }
}

codex mcp add pdf-mcp -- pdf-mcp

Or configure manually in ~/.codex/config.toml:

[mcp_servers.pdf-mcp]
command = "pdf-mcp"

Create or edit .kiro/settings/mcp.json in your workspace:

{
  "mcpServers": {
    "pdf-mcp": {
      "command": "pdf-mcp",
      "args": [],
      "disabled": false
    }
  }
}

Save and restart Kiro.

Most MCP clients use a standard configuration format:

{
  "mcpServers": {
    "pdf-mcp": {
      "command": "pdf-mcp"
    }
  }
}

With uvx (for isolated environments):

{
  "mcpServers": {
    "pdf-mcp": {
      "command": "uvx",
      "args": ["pdf-mcp"]
    }
  }
}

Verify Installation

pdf-mcp --help

Tools

The typical pattern: call pdf_info first to plan, then pdf_search to locate — its paragraph excerpts are often enough to answer directly. Use pdf_read_pages or pdf_read_all when you need deeper context.

Tool	What it does
pdf_info	Page count, metadata, TOC summary, scanned-page detection. Call first.
pdf_get_toc	Full table of contents for documents with >50 bookmarks
pdf_read_pages	Read specific pages or ranges; OCR-on-demand; embedded images + tables
pdf_read_all	Read entire document in one call (byte-capped for safety)
pdf_render_pages	Render pages as PNG for vision models — diagrams, handwriting, scans
pdf_search	Hybrid RRF search (keyword + semantic), page or section granularity, optional paragraph excerpts
pdf_cache_stats	Per-document cache breakdown + total size
pdf_cache_clear	Clear expired or all cache entries
server_info	Which optional features (column-aware, OCR, semantic) and config are active. Call before feature-dependent calls.

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"

See docs/tool-reference.md for the complete reference — every parameter, response shape, security contract, and example. For semantic-search model selection, see 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

Configuration

Access control (optional)

Create ~/.config/pdf-mcp/config.toml to restrict which local paths and URL hosts the server will access. The file is optional — if absent, the server is permissive within the built-in SSRF floor (HTTPS-only, blocked private IP ranges).

[paths]
allow = ["~/Documents/**", "/data/pdfs/**"]
deny  = ["~/.ssh/**", "~/.aws/**"]

[urls]
allow = ["*.internal.example.com"]
deny  = ["untrusted.example.com"]

[limits]
max_response_bytes = 200000

The [limits] block caps text-payload byte size on pdf_read_all and section-granularity pdf_search — see docs/response-limits.md. Rules use shell-glob patterns (* matches across path separators). deny wins when both match. Path matching operates on the resolved path after symlink expansion. A malformed config file prevents the server from starting — it never silently falls back to permissive.

Environment variables

# Cache directory (default: ~/.cache/pdf-mcp)
PDF_MCP_CACHE_DIR=/path/to/cache

# Cache TTL in hours (default: 24)
PDF_MCP_CACHE_TTL=48

# Max worker processes for parallel OCR / rendering in pdf_read_pages
# (default: auto = min(cpu_count, pages, 8)). Set to 1 to force sequential.
PDF_MCP_MAX_WORKERS=8

Caching

The server uses SQLite for persistent caching.

Cache location: ~/.cache/pdf-mcp/cache.db

What's cached:

Data	Benefit
Metadata + text coverage	Avoid re-parsing document info
Page text	Skip re-extraction
Images	Skip re-encoding
Tables	Skip re-detection
TOC	Skip re-parsing
FTS5 index	O(log N) search with BM25 ranking after first query
Embeddings	Instant semantic search after first indexing run
Rendered PNGs	Skip re-rendering; shared between pdf_render_pages and pdf_read_pages(render_dpi=…)

Cache invalidation:

• Automatic when file modification time changes
• Manual via the pdf_cache_clear tool
• TTL: 24 hours (configurable)

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.

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

• pdf-mcp on PyPI
• pdf-mcp on GitHub

Blog posts

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

Getting started

• How I Built pdf-mcp — The problem with large PDFs in AI agents and a working solution
• How Claude Code Actually Reads PDFs — How AI agents use pdf-mcp tools to read and navigate PDF documents

Search & retrieval

• Semantic vs Keyword Search for AI Agents — Benchmarks and a dual-search routing pattern: FTS5 for exact identifiers, embeddings for natural language
• Hybrid Search vs Query Routing for AI Agents — Why pdf-mcp uses hybrid RRF instead of query routing: benchmarks showing RRF wins across query types
• Section Chunking vs Page Chunking for AI Agents — Why section-aware search delivers full section content in one call while page-mode costs 2–6 extra tool calls per query
• Section-Level RAG: Why BM25 Beat Hybrid Search in My Benchmark — Why pdf-mcp's section-grain search is BM25-only: hybrid RRF caused a 33% lexical regression at section grain, so granularity decides the search technique

Engineering & security

• MCP Server Security: 8 Vulnerabilities — What we found when we audited an MCP server for security holes
• Your LLM Is Free QA for Your MCP Server — Four Payload UX bugs in pdf-mcp that schema tests missed but Claude Desktop surfaced during real use