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PDF Reader MCP Server

by sylphxltd
index.md2.11 kB
# Design Philosophy The PDF Reader MCP Server is built upon several core principles: 1. **Security First:** - **Context Confinement:** The absolute primary goal. All local file access _must_ be restricted to the directory (and its subdirectories) where the server process is launched. This prevents the AI agent from accessing unintended files on the user's system. - **Path Validation:** Rigorous validation of all incoming paths using a dedicated `resolvePath` function ensures they are relative and resolve within the designated project root. - **No Arbitrary Execution:** The server only performs PDF reading operations, not arbitrary file system modifications or command execution. 2. **Efficiency & Resourcefulness:** - **Structured Data:** Instead of sending potentially huge raw PDF content (which is often impractical for LLMs), the server extracts specific, structured information (text, metadata, page count). - **Targeted Extraction:** Allows requesting text from specific pages, minimizing the amount of data transferred and processed. - **Asynchronous Operations:** Uses Node.js async I/O to avoid blocking the event loop during file access and PDF parsing. 3. **Simplicity & Ease of Integration:** - **Single Tool Focus:** Consolidates functionality into a single `read_pdf` tool with clear parameters, making it easier for AI agents to learn and use. - **Standard MCP:** Leverages the `@modelcontextprotocol/sdk` for standard communication and error handling. - **Clear Schemas:** Uses Zod for defining and validating input, providing clear contracts for tool usage. - **Multiple Invocation Methods:** Supports easy use via `npx` or Docker for straightforward deployment in various MCP host environments. 4. **Minimalism & Reliability:** - **Minimal Dependencies:** Relies primarily on the robust and widely-used `pdfjs-dist` library for core PDF parsing, minimizing external failure points. - **Clear Error Reporting:** Provides specific error messages when processing fails for a source, allowing the agent to understand the issue.

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