PDF MCP Server
An MCP server for PDF form filling, basic editing, and OCR text extraction. It enables users to merge, rotate, annotate, and sign PDFs, while also supporting text extraction from both searchable and scanned image-based documents.
README
PDF MCP Server
MCP server for PDF form filling, basic editing (merge, extract, rotate, flatten), and OCR text extraction. Built with Python, pypdf, fillpdf, and pymupdf (AGPL).
Goal: Extract 99% of information from any PDF file, including scanned/image-based documents, and fill any PDF forms.
Status
CI notes
- Dependency Review requires GitHub Dependency Graph to be enabled in the repository settings.
- AI Review is optional and only runs if you add the
OPENAI_API_KEYrepository secret.
Setup (uv)
- Install
uvif not present:
curl -Ls https://astral.sh/uv/install.sh | sh
- Install dependencies (project root is this folder):
cd /path/to/pdf-mcp-server
uv pip install -r requirements.txt
Or use the Makefile:
cd /path/to/pdf-mcp-server
make install
For best flatten support, install Poppler:
sudo apt-get install poppler-utils
OCR Support (Optional)
For OCR capabilities on scanned/image-based PDFs, install Tesseract:
macOS:
brew install tesseract
pip install pytesseract pillow
Linux (Ubuntu/Debian):
sudo apt-get install tesseract-ocr
pip install pytesseract pillow
Or install with the ocr extra:
pip install -e ".[ocr]"
Run the MCP server
python -m pdf_mcp.server
(It runs over stdio by default.)
Register with Cursor
Edit ~/.cursor/mcp.json:
{
"mcpServers": {
"pdf-handler": {
"command": "/path/to/pdf-mcp-server/.venv/bin/python",
"args": ["-m", "pdf_mcp.server"],
"description": "Local PDF form filling and editing (stdio)"
}
}
}
Restart Cursor after saving.
Available tools (initial)
get_pdf_form_fields(pdf_path): list fields and count.fill_pdf_form(input_path, output_path, data, flatten=False): fill fields; optional flatten (uses fillpdf if available, else pypdf fallback).clear_pdf_form_fields(input_path, output_path, fields=None): clear (delete) values for selected form fields while keeping fields fillable.flatten_pdf(input_path, output_path): flatten forms/annotations.merge_pdfs(pdf_list, output_path): merge multiple PDFs.extract_pages(input_path, pages, output_path): 1-based pages, supports negatives (e.g., -1 = last).rotate_pages(input_path, pages, degrees, output_path): degrees must be multiple of 90.add_text_annotation(input_path, page, text, output_path, rect=None, annotation_id=None): add a FreeText annotation (managed text insertion).update_text_annotation(input_path, output_path, annotation_id, text, pages=None): update an annotation by id.remove_text_annotation(input_path, output_path, annotation_id, pages=None): remove an annotation by id.remove_annotations(input_path, output_path, pages, subtype=None): remove annotations on pages, optionally filtered by subtype (example FreeText).insert_pages(input_path, insert_from_path, at_page, output_path): insert all pages from another PDF before at_page (1-based).remove_pages(input_path, pages, output_path): remove specific 1-based pages.insert_text(input_path, page, text, output_path, rect=None, text_id=None): insert text via a managed FreeText annotation.edit_text(input_path, output_path, text_id, text, pages=None): edit managed inserted text.remove_text(input_path, output_path, text_id, pages=None): remove managed inserted text.get_pdf_metadata(pdf_path): return basic PDF document metadata.set_pdf_metadata(input_path, output_path, title=None, author=None, subject=None, keywords=None): set basic metadata fields.add_text_watermark(input_path, output_path, text, pages=None, rect=None, annotation_id=None): add a simple text watermark or stamp via FreeText annotations.add_comment(input_path, output_path, page, text, pos, comment_id=None): add a PDF comment (Text annotation, sticky note).update_comment(input_path, output_path, comment_id, text, pages=None): update a PDF comment by id.remove_comment(input_path, output_path, comment_id, pages=None): remove a PDF comment by id.add_signature_image(input_path, output_path, page, image_path, rect): add a signature image to a page (returnssignature_xref).update_signature_image(input_path, output_path, page, signature_xref, image_path=None, rect=None): update or resize a signature image.remove_signature_image(input_path, output_path, page, signature_xref): remove a signature image.encrypt_pdf(input_path, output_path, user_password, owner_password=None, ...): encrypt (password-protect) a PDF (use afteradd_signature_imageto protect a signed PDF).
OCR and Text Extraction Tools
detect_pdf_type(pdf_path): analyze PDF to classify as "searchable", "image_based", or "hybrid"; returns page-by-page metrics and OCR recommendation.extract_text_native(pdf_path, pages=None): extract text using native PDF text layer only (fast, no OCR).extract_text_ocr(pdf_path, pages=None, engine="auto", dpi=300, language="eng"): extract text with OCR fallback; engine options: "auto" (native→OCR), "native", "tesseract", "force_ocr".get_pdf_text_blocks(pdf_path, pages=None): extract text blocks with bounding box positions (useful for form field detection).
Conventions
- Paths should be absolute; outputs are created with parent directories if missing.
- Inputs must exist and be files; errors return
{ "error": "..." }. - Form flattening prefers fillpdf+poppler; falls back to a pypdf-only flatten (removes form structures).
- Text insert/edit/remove is implemented via managed FreeText annotations, not by editing PDF content streams.
Smoke tests (manual)
python - <<'PY'
from pdf_mcp import pdf_tools
sample = "/path/to/sample.pdf"
out = "/tmp/out.pdf"
print(pdf_tools.get_pdf_form_fields(sample))
print(pdf_tools.fill_pdf_form(sample, out, {"Name": "Test"}, flatten=True))
PY
Automated tests
cd /path/to/pdf-mcp-server
make test
Development workflow
- Use feature branches off
mainand open a PR for review. - Keep each PR focused on a single tool or capability with tests.
- For larger features, split into small PRs (tool surface, core implementation, tests, docs).
- After merging a PR, delete the feature branch and run
git fetch --prunelocally to keep branch state clean. - Portability/migration notes: see
PROJECT_MEMO/.
License
GNU AGPL-3.0, see LICENSE.
Changelog
See CHANGELOG.md.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.