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"Analyzing Large Amounts of Text" matching MCP servers:

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    Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.
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    MIT
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    An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.
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    Apache 2.0
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    A lightweight knowledge base MCP server that enables full-text search and retrieval of markdown documents from indexed sites using Orama BM25.
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    Apache 2.0
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    A persistent, cross-session knowledge base for AI agents that indexes session history into a searchable SQLite database with full-text search, enabling recall of past sessions, stored knowledge, and summaries.
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    Enables exploration and organization of research ideas using a hierarchical tile-based method with support for creating interconnected nodes (questions, hypotheses, methods, results), analyzing research gaps, and exporting to multiple visualization formats.
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    MIT
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    A local MCP server that provides agents with tools to list, read, search, inspect history and diffs, and capture unstructured text in a user-owned Git repository of durable memory.
    5
    MIT
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    A project-agnostic Open Knowledge Format MCP server that indexes Markdown concepts with YAML frontmatter and provides CLI and MCP tools for search, validation, and graph navigation of structured knowledge.
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    MIT
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    Enables searching and browsing of Claude conversation history with hybrid search combining full-text, Spotlight, and optional vector embeddings.
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    MIT
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    Provides access to Wikidata for Large Language Models through the Model Context Protocol, offering tools for entity search, detailed retrieval, SPARQL queries, relation exploration, and property-based searches.
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    MIT
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    Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.
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    MIT
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    AoT MCP server enables AI models to solve complex reasoning problems by decomposing them into independent, reusable atomic units of thought, featuring a powerful decomposition-contraction mechanism that allows for deep exploration of problem spaces while maintaining high confidence in conclusions.
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    MIT
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    An MCP server that connects to Tana's Input API, allowing Large Language Models and other MCP clients to create and manipulate data in Tana workspaces.
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    MIT
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    A read-only MCP server that lets AI agents search and retrieve the Wheel of Heaven corpus, including source-grounded facts, interpretations, and comparative traditions, all with full epistemic metadata.
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    Creative Commons Zero v1.0 Universal
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    iwec gives AI agents structured access to your Markdown notes as a knowledge graph instead of a pile of files. Agents search, retrieve, traverse, and refactor your notes by structure and links — not blind text similarity — working from the same plain .md files you edit by hand. No database, no cloud, no lock-in.
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    Apache 2.0