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"Write TypeScript Code to Work with Local LLM" matching MCP servers:

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    Enables Claude to write in your personal style by learning from your local documents. It provides statistical style context for natural language rewriting, all without any data leaving your machine.
    MIT
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    MCP server that provides AI assistants with real-time, current information about AI models from OpenAI, Anthropic, and Google, including pricing, capabilities, and context windows.
    4
    MIT
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    MCP server for grounded, cited AI: answers questions from live web sources, verifies claims, fact-checks documents, searches and reads URLs, summarises, classifies, and extracts fields, with usage tracking and status.
    MIT
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    A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.
    7
    MIT
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    Monitors brand mentions, citations, sentiment, competitor share of voice, and GEO performance across AI search engines.
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    MIT
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    A local CrossRef database MCP server enabling full-text search across 167M+ scholarly works, citation analysis, and impact factor retrieval without rate limits or internet dependency.
    17
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    AGPL 3.0
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    Connects AI systems to Local Falcon API, enabling access to local SEO reporting tools including scan reports, trend analysis, keyword tracking, and competitor data through the Model Context Protocol.
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    MIT
  • F
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    Enables querying WolframAlpha's LLM API for natural language questions, providing structured and simplified answers optimized for LLM consumption.
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    Provides a tool to fetch real-time cryptocurrency prices from the Binance API and logs server activity to file resources. It allows users to query crypto symbols and monitor MCP interactions through natural language interfaces.
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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.
    1
    MIT
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    Provides semantic code search and code insights via a knowledge graph, enabling AI to understand, navigate, and modify complex projects with deep dependency and architecture analysis.
    MIT
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    Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.
    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.
    1
    MIT
  • F
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    Local knowledge base built from PDF lecture slides on Agentic Coding, exposed as an MCP server with search, page retrieval, and query tools.