mcp-long-context-reader

# MCP Long Context Reader <!-- [](./docs/build/html/index.html) --> MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to "read" and query long documents without needing to load the entire text into its context window. This project features an intelligent, filesystem-based caching backend. When a document is processed for the first time with a specific strategy, the expensive work (like generating embeddings) is cached. Subsequent queries on the same document are significantly faster. ## Features This toolkit provides five distinct strategies as MCP tools: 1. **`glance`**: Provides a quick look at the beginning of a file, showing the first few thousands characters and total line count. 2. **`search_with_regex`**: Finds and extracts text snippets using regular expression patterns. Ideal for precise, pattern-based lookups. 3. **`retrieve_with_rag`**: Uses a Retrieval-Augmented Generation (RAG) pipeline to find the most semantically relevant document chunks based on a natural language question. 4. **`summarize_with_map_reduce`**: A classic "divide and conquer" strategy that summarizes large chunks in parallel and then combines those summaries. Best for getting the gist of a very long document. 5. **`summarize_with_sequential_notes`**: An advanced strategy where an LLM reads the document sequentially, taking query-aware notes. Best for tasks requiring strict order and detail sensitivity (e.g., "needle-in-a-haystack"). ## Getting Started ### 1. Prerequisites - Python 3.10 or newer. - `uv` Python package manager. If you don't have it, install it with `pip install uv`. - An [OpenAI API Key](https://platform.openai.com/api-keys) for the RAG and LLM-based strategies. ### 2. Environment S…