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"Working with Excel" matching MCP servers:

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    Enables querying Excel and CSV files using SQL via natural language, allowing AI assistants to analyze data without manual SQL writing.
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    MIT
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    Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
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    MIT
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    Unofficial MCP server for working with Kagi without API access (you'll need to be a customer, tho). Searches and summarizes. Uses Kagi session token for easy authentication.
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    MIT
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    Provides AI assistants with 69 tools, 19 prompts, and 21 resources for deep access to Magic: The Gathering, including card data, combos, draft analytics, Commander metagame, competitive constructed, sideboard strategy, deck building, and rules engine, working with any MCP client.
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    MIT
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    Enables working with large documents of any size by intelligently segmenting them and using TF-IDF search to retrieve only relevant fragments, preventing context window saturation. Provides 31 domain-agnostic tools for document ingestion, semantic analysis, epistemological validation, and extraction verification across formats like PDF, EPUB, and HTML.
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    MIT
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    A precision code-retrieval MCP server for coding agents working in large, legacy, and air-gapped codebases. It returns exact file and line range citations from natural-language queries without requiring the agent to perform blind searches.
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    MIT
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    A Model Context Protocol server that enhances LLM capabilities by connecting to Wikipedia, internet search (Tavily), and financial data (Yahoo Finance) tools, enabling contextual responses to user queries.
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    Enables AI assistants to query databases using natural language, with automatic schema discovery and SQL compilation.
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    Apache 2.0
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    Enables natural language interaction with PostgreSQL databases through Amazon Q for querying data, listing tables, and describing schemas. It provides secure, read-only access with automatic row limits to ensure efficient database exploration.
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    Apache 2.0
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    Webhound is a research engine with an explicit effort control: a prompt defines the investigation and a dollar budget defines how much work it deserves. It returns cited reports or sourced datasets with the working documents, claims, sources, limitations, and evidence pack behind the answer.
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    MIT
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    Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.
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    MIT