PDF Reader MCP Server

The PDF Reader MCP Server bridges the gap between AI assistants and static PDF content. In many knowledge‑base or documentation workflows, PDFs are a primary source of information—research reports, user manuals, legal documents, and more. However, AI agents typically lack built‑in capabilities to parse these files securely and efficiently. This server solves that problem by exposing a single, well‑defined tool () that extracts text, metadata, and page counts from PDFs located within a project’s root directory or reachable via public URLs. By confining access to the project root, it mitigates accidental exposure of sensitive files while still allowing developers to tap into rich document data.

For developers building AI‑powered assistants, the server offers a streamlined integration path. An assistant can invoke with minimal context: specify file paths, desired pages, and flags for metadata or full‑text extraction. The server returns structured JSON that the assistant can immediately consume, feed into downstream prompts, or store in a vector database. This eliminates the need for custom PDF parsing logic in each host application, centralizes security policies, and ensures consistent output across different environments.

Key capabilities include:

• Selective page extraction: Retrieve only the pages that matter, reducing payload size and processing time.
• Metadata retrieval: Access author, creation date, and other document properties without additional tooling.
• Page count reporting: Quickly determine the length of a PDF for pagination or validation purposes.
• URL support: Fetch and parse PDFs hosted on the web, enabling dynamic content ingestion.
• Secure sandboxing: All file operations are bounded to the project root, preventing directory traversal or unintended reads.

Typical use cases span from building a knowledge‑base assistant that pulls excerpts from corporate reports, to automating compliance checks by scanning legal PDFs for specific clauses, or powering a chatbot that can answer questions about the contents of a user‑uploaded manual. In research settings, the server can feed extracted text into NLP pipelines for summarization or sentiment analysis without exposing raw PDFs to the model.

By encapsulating PDF parsing in a dedicated MCP server, developers gain a single point of maintenance, consistent security controls, and an API that fits naturally into existing AI workflows. Whether you’re running the server via npm, Docker, or a local build, the tool delivers reliable, secure access to PDF content—making it a standout component for any AI‑centric application that needs to read and understand documents.