pdf-mcp

A Model Context Protocol (MCP) server that enables AI agents to read, search, and extract content from PDF files. Built with Python and PyMuPDF, with SQLite-based caching for persistence across server restarts.

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

Try it in your browser

See what your AI agent sees →

Walk through the three main tools (pdf_info, pdf_search, pdf_read_pages) with any PDF. 100% client-side, no install required.

<img src="https://raw.githubusercontent.com/jztan/pdf-mcp/develop/docs/images/demo.png" alt="pdf-mcp browser demo: pdf_search returns page-level matches with highlighted excerpts" width="720">

Features

Give your agent surgical access to PDFs instead of flooding context with raw text.

• 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
• 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

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 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:

<details open> <summary><strong>Claude Code</strong></summary>

claude mcp add pdf-mcp -- pdf-mcp

Or add to ~/.claude.json:

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

</details>

<details> <summary><strong>Claude Desktop</strong></summary>

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.

</details>

<details> <summary><strong>Visual Studio Code</strong></summary>

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"
    }
  }
}

</details>

<details> <summary><strong>Codex CLI</strong></summary>

codex mcp add pdf-mcp -- pdf-mcp

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

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

</details>

<details> <summary><strong>Kiro</strong></summary>

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

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

Save and restart Kiro.

</details>

<details> <summary><strong>Other MCP Clients</strong></summary>

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"]
    }
  }
}

</details>

Verify Installation

pdf-mcp --help

Tools

Eight specialized tools cover document introspection, content reading, search, and cache management. 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

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

Caching

The server uses SQLite for persistent caching. This is necessary because MCP servers using STDIO transport are spawned as a new process for each conversation.

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)

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

Development

git clone https://github.com/jztan/pdf-mcp.git
cd pdf-mcp

# Install with dev dependencies
pip install -e ".[dev]"

# One-time: install pre-commit hooks (auto-runs black/flake8/mypy on commit)
pre-commit install

# Run tests
pytest tests/ -v

# Type checking
mypy src/

# Linting
flake8 src/ tests/

# Formatting
black src/ tests/

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 — RRF fusion of BM25 keyword (FTS5) + semantic embeddings; never misses what either alone would
Tables	Lost in raw text	Extracted and inlined per page
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	8 specialized tools

Roadmap

See ROADMAP.md for planned features and release history.

Contributing

Contributions are welcome. Please submit a pull request.

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
• How I Built pdf-mcp — The problem with large PDFs in AI agents and a working solution
• MCP Server Security: 8 Vulnerabilities — What we found when we audited an MCP server for security holes
• How Claude Code Actually Reads PDFs — How AI agents use pdf-mcp tools to read and navigate PDF documents
• 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
• 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