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"Using Logseq data as context for chat applications" matching MCP servers:

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    Enables AI assistants like Claude to directly read, write, search, and navigate your local Logseq knowledge graph, including managing journals, pages, backlinks, and page relationships without manual copy-pasting.
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    6
    MIT
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    Transforms chat conversations with AI into structured markdown summaries and automatically saves them to organized files in your notes directory. Supports different summary styles, handles large conversations through chunking, and provides tools to manage your saved summaries.
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    50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.
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    5
    MIT
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    An MCP server that exposes macOS Spotlight search as tools, wrapping mdfind and mdls to provide full-text and metadata file search across the system.
    2
    MIT
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    Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.
    3
    10
    MIT
  • F
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    quality
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    Enables interactive chat with a local LLM using MCP architecture for document management, including tools to read, edit, and format documents.
    2
  • A
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    quality
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    A unified context layer that connects your local data — repositories, documents, remote machines, and notes — to LLM interfaces through the Model Context Protocol (MCP).
    3
    MIT
  • A
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    A collection of small MCP servers for macOS automation that expose Apple Mail, MoneyMoney, and Spotlight functionality as tools via AppleScript and command-line, enabling AI applications to search mail, manage contacts and transactions, and perform system-wide file searches through natural language.
    MIT
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    Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.
    5
    MIT
  • F
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    quality
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    A thin, local, read-only MCP gateway that exposes governed context documents and producer manifests as logical resources, enabling AI clients to access them without arbitrary filesystem access.
    1
  • A
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    Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.
    3
    6
    MIT
  • A
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    MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.
    3
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    8
    MIT
  • A
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    quality
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    MCP server that profiles local data files (CSV, Parquet, JSON, Excel) and returns compact structured summaries with data-quality flags, enabling AI agents to understand datasets without seeing raw rows.
    1
    1
    MIT