PDF Reader MCP

The PDF Reader MCP fills a critical gap for AI assistants that need to ingest, interrogate, and analyze PDF documents in real time. Traditional document‑reading tools are often tightly coupled to local environments, require heavy dependencies, or expose limited search capabilities. This server exposes a clean, asynchronous interface that lets an AI client extract text, perform sophisticated searches, and pull rich metadata—all while safeguarding against oversized files and ensuring secure file handling.

At its core, the server offers three specialized tools:

• – Pulls raw or cleaned text from any page range, optionally attaching metadata. It supports granular extraction so developers can target specific sections of a report or a legal brief without parsing the entire file.
• – Enables targeted queries with options for case sensitivity, whole‑word matching, and regex support. This turns a static document into an interactive knowledge base that the assistant can reference on demand.
• – Returns a comprehensive snapshot of the PDF’s properties, including author, creation date, encryption status, and more. This is invaluable for audit trails or when the assistant needs to verify document provenance.

These capabilities are delivered through a non‑blocking, file‑size‑restricted API (50 MB limit) that protects server resources and ensures predictable latency. The server’s architecture is intentionally lightweight, allowing it to run as a child process or within containerized environments without imposing heavy runtime overhead.

Real‑world use cases abound: a legal AI assistant can quickly fetch the exact paragraph that cites a precedent; a data analyst can locate all instances of a KPI across quarterly reports; an academic chatbot can pull metadata to auto‑populate citation fields. By integrating this MCP into a broader workflow—such as a document ingestion pipeline or an interactive Q&A system—developers can turn static PDFs into dynamic, searchable knowledge assets without reinventing parsing logic.

Unique advantages include:

• Fine‑grained control over page ranges and text cleaning, giving developers the flexibility to balance speed against fidelity.
• Robust security through path sanitization and file validation, mitigating common injection risks associated with file processing.
• Extensible foundation: the planned roadmap (OCR, image extraction, table detection) means the server can evolve alongside emerging document‑analysis needs without breaking existing contracts.

In short, the PDF Reader MCP transforms PDFs from passive files into active participants in AI workflows, delivering precise text extraction, powerful search, and rich metadata—all wrapped in a secure, asynchronous interface that developers can trust.