@jaesharp
Basic information
A document reading, search, and metadata server to provide access to PDFs (and, in the future, other formats) to LLMs via the Model Context Protocol (MCP)
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"document-understanding-mcp-server": {
"command": "uv",
"args": [
"venv",
"#",
"Create",
".venv"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
A Model Context Protocol (MCP) server that provides tools for extracting text, metadata, layout, tables, and images from PDF documents, with OCR fallback for scanned pages. It is currently in alpha stage and intended for local development and testing only.
Install Python 3.11+, uv, sync dependencies from pyproject.toml, and install Java (for table extraction) and Tesseract (for OCR). Set the required environment variable DOCUMENT_UNDERSTANDING_BASE_PATH to the directory the server may read PDFs from. The server offers tools that AI models can invoke via the MCP interface.
tabula-pyYes, table extraction requires a Java Runtime Environment (JRE). The OCR fallback for scanned pages requires Tesseract OCR. The server will refuse to start if either is missing, unless you use `--allow-no-java` or `--allow-no-tesseract` flags.
It is the absolute path to the directory the server is allowed to read PDFs from. The server will not start without this variable, unless the `--allow-any-path` security risk flag is used.
No. The README explicitly warns it is in alpha stage and should not be deployed in production or exposed to untrusted networks.
Yes, several tools accept an optional `password` argument to open encrypted PDFs.
Use the `--enable-experimental` command-line flag at startup to enable tools like `find_nearby_content`.
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