Claude Chunks is an intelligent document chunking MCP server optimized for Claude context windows. It splits large documents into meaningful, context-aware chunks that can be progressively processed by Claude while maintaining connections between sections and formatting output for optimal reuse.
Claude Chunks helps in breaking down large documents (like books, theses, or long papers) into manageable sections, generating summaries for each section, preserving context, and optimizing the output for efficient processing by Claude.
Researchers, writers, data analysts, and anyone dealing with large text-based documents who want to process and summarize content effectively using Claude's capabilities will benefit from Claude Chunks.
Claude Chunks can be installed locally on your machine by cloning its GitHub repository and setting it up as an MCP server within Claude Desktop configuration.
You should use Claude Chunks when working with lengthy documents that require structured chunking, summarization, and contextual preservation to improve progressive analysis and integration with Claude.
To install Claude Chunks, clone the repository using 'git clone https://github.com/vetlefo/claude-chunks.git', navigate into the directory, run 'npm install' to add dependencies, and then build the project with 'npm run build'.
Add the MCP server details to your Claude Desktop config file under `mcpServers`, specifying the path to the compiled script, and restart Claude Desktop to enable the tool.
The roadmap includes Phase 1: Core Functionality (basic setup, chunking, summarization), Phase 2: Enhancements (multiple formats, custom strategies), and Phase 3: Advanced Features (cross-references, theme detection).
Yes! Contributions are welcome. Please refer to the Contributing Guide provided in the repository for more details.
Claude Chunks operates under the MIT License.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.