@modelcontextprotocol/server-pdf
MCP ServerFreeMCP server for loading and extracting text from PDF files with chunked pagination and interactive viewer
Capabilities6 decomposed
pdf text extraction with streaming chunked output
Medium confidenceExtracts text content from PDF files and returns it in configurable chunks via MCP resource protocol, enabling progressive streaming of large documents without loading entire file into memory. Uses a chunking strategy that respects document structure (pages, sections) rather than naive byte-splitting, allowing clients to consume content incrementally and implement pagination UI.
Implements MCP resource protocol for PDF access, allowing LLM clients to request specific chunks by index rather than re-parsing entire documents, with built-in pagination metadata that tracks source page numbers and chunk boundaries
Provides native MCP integration for seamless LLM context management versus generic PDF libraries that require manual chunking and context window management in application code
interactive pdf viewer resource exposure
Medium confidenceExposes PDF documents as MCP resources with metadata (page count, chunk boundaries, file size) that enables LLM-powered clients to render interactive viewers with AI-assisted navigation. The server maintains resource URIs and metadata that clients can use to build UI components that jump to specific pages or chunks, with server-side state tracking of document structure.
Leverages MCP resource protocol to expose PDFs as first-class resources with queryable metadata, allowing clients to build stateless viewer UIs that request specific chunks by reference rather than managing document state themselves
Differs from file-serving approaches by providing semantic document structure (page boundaries, chunk indices) through MCP, enabling LLMs to reason about document navigation rather than treating PDFs as opaque blobs
page-aware text chunking with boundary preservation
Medium confidenceSplits PDF text into chunks that respect page boundaries and configurable chunk sizes, maintaining metadata about which page each chunk originated from. Uses a two-pass algorithm: first identifies page breaks in the extracted text, then applies chunking within page boundaries to avoid splitting content across pages when possible, with fallback to cross-page chunks only when a single page exceeds chunk size limit.
Implements page-boundary-aware chunking that preserves page context metadata for each chunk, enabling RAG systems to maintain citation links back to source pages without post-processing
More sophisticated than naive fixed-size chunking because it respects document structure (page breaks) and maintains source attribution, versus generic text splitters that lose document context
mcp server protocol implementation for pdf resources
Medium confidenceImplements the Model Context Protocol (MCP) server specification to expose PDF documents as queryable resources that LLM clients can request via standardized MCP calls. Handles MCP resource listing, resource content retrieval, and metadata queries through the MCP transport layer (stdio, HTTP, or WebSocket), allowing any MCP-compatible client (Claude, custom agents) to access PDFs without direct file system access.
Provides a complete MCP server implementation that bridges PDFs into the MCP ecosystem, allowing LLMs to treat PDFs as first-class resources via standardized protocol calls rather than requiring custom API wrappers
Enables seamless integration with MCP-native tools and LLMs (Claude, custom agents) versus custom REST APIs that require per-client integration and lack standardized resource semantics
batch pdf processing with resource caching
Medium confidenceSupports loading multiple PDF files and exposing them as a collection of MCP resources with server-side caching of parsed content. When a PDF is first requested, the server extracts and chunks the text, caches the result in memory, and serves subsequent requests from cache without re-parsing. Implements cache invalidation based on file modification time to detect when source PDFs have changed.
Implements transparent in-process caching with file modification tracking, allowing the server to serve cached PDFs without re-parsing while automatically detecting source file changes
More efficient than re-parsing PDFs on every request, but simpler than external cache systems (Redis) because it uses in-process memory and file mtime for invalidation without additional infrastructure
pdf metadata extraction and document structure analysis
Medium confidenceExtracts and exposes PDF metadata (title, author, creation date, page count, embedded fonts, encoding) and analyzes document structure (page breaks, section boundaries, table of contents if available) to provide semantic context about the document. Uses PDF parsing libraries to read metadata streams and infer structure from text layout and formatting information, exposing this as queryable MCP resource metadata.
Exposes PDF metadata and inferred structure as queryable MCP resource properties, allowing LLM clients to reason about document characteristics before requesting full text extraction
Provides semantic document understanding beyond raw text extraction, enabling smarter document routing and summarization versus treating PDFs as opaque content blobs
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓LLM application developers building document processing pipelines
- ✓Teams building RAG systems that need to ingest PDF documents
- ✓Developers creating interactive PDF viewers with AI assistance
- ✓Frontend developers building document collaboration tools
- ✓Teams creating AI-assisted document review interfaces
- ✓Developers integrating PDFs into LLM chat applications with visual context
- ✓RAG pipeline developers building citation-aware document indexing
- ✓Teams implementing document Q&A systems that need source attribution
Known Limitations
- ⚠No support for scanned PDFs or image-based content — requires text-layer PDFs
- ⚠Chunking strategy is fixed and not customizable per document type
- ⚠No preservation of document formatting, tables, or layout information — returns plain text only
- ⚠Performance degrades on PDFs with complex embedded fonts or unusual encoding
- ⚠No built-in rendering — server only exposes metadata and text; client must implement UI
- ⚠No support for PDF annotations, comments, or form fields
Requirements
Input / Output
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MCP server for loading and extracting text from PDF files with chunked pagination and interactive viewer
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