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

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    Bridges local LLMs running in LM Studio with MCP clients like Claude Desktop to perform reasoning and analysis tasks while keeping sensitive data private. It features a suite of tools for local code review, privacy scanning, and content transformation using auto-discovered local models.
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    MIT
    • Apple
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    Enables recording, querying, and summarizing daily work entries with tags using a local SQLite database. Supports work logging, search, timeline queries, tag management, and automated reminders for tracking daily tasks.
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    Enables automatic creation and management of weekly work reports in Korean through structured templates. Organizes work tasks and issues with automatic file management, backup/rollback, and 100% template compliance through JSON Schema validation.
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    A Model Context Protocol server that enables AI tools to automatically log their activities with detailed metrics like timestamps, token usage, and costs into daily markdown worklog files.
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    MIT
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    A privacy-first MCP server that provides local LLM-enhanced tools for code analysis, security scanning, and automated task execution using backends like Ollama and LM Studio. It enables symbol-aware code reviews and workspace exploration while ensuring that all code and analysis remain strictly on your local machine.
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    ISC
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    Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.
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    MIT
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    A 100% local development monitoring tool that captures browser console logs, network requests, and backend server output for analysis by AI assistants via MCP. It enables LLMs to debug applications by providing structured, real-time access to full-stack log data and persistent local storage.
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    5
    MIT
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    Enables AI agents to interact with multiple LLM providers (OpenAI, Anthropic, Google, DeepSeek) through a standardized interface, making it easy to switch between models or use multiple models in the same application.
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    A lightweight MCP server that enables LLMs to search the web via DuckDuckGo, search GitHub code repositories, and extract clean content from web pages in LLM-friendly formats.
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    Routes your AI tasks to the best available model across 20+ providers — automatically selecting based on task type, budget, and subscription pressure. Supports text, image, video, and audio with built-in cost optimization and fallback chains.
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    MIT
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    LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.
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    MIT
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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
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    A Model Context Protocol (MCP) server that enables AI applications to access and analyze local code repositories without manual uploads, providing file listing, content reading, code searching, and project structure analysis capabilities.
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    • Apple
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    Provides LLMs with safe, read-only access to local codebases for searching, reading files, and finding function definitions. All source code remains local, ensuring privacy while enabling AI assistants to explore project structures and functionality.
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    A Model Context Protocol server that provides unified access to multiple LLM APIs including ChatGPT, Claude, and DeepSeek, allowing users to call different LLMs from MCP-compatible clients and combine their responses.
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    MIT
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    Enables AI assistants to search, read, create, and modify Markdown notes in local Obsidian vaults directly through filesystem operations. Supports tag-based discovery and frontmatter parsing without requiring Obsidian to be open, facilitating integration with VS Code Copilot via stdio transport.
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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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    A specialized server that provides advanced TypeScript code manipulation and analysis capabilities, enabling refactoring, navigation, diagnostics, and module analysis through Claude.
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    MIT
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    A local Model Context Protocol server designed to share contextual information between an AI and a user. It primarily provides a tool to retrieve the current date and time in ISO 8601 format based on the server's local timezone.
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    Provides semantic code search capabilities that run 100% locally using EmbeddingGemma embeddings. Enables finding code by meaning across 15 file extensions and 9+ programming languages without API costs or sending code to the cloud.
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    Enables AI assistants to intelligently search and explore local file systems using native Unix commands (ripgrep, find, ls) with token-optimized output, automatic pagination, and multi-layer security validation.
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    MIT
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    Provides 18 tools including calculator operations (add, subtract, multiply, divide, power, sqrt, log, trigonometric functions) and secure sandboxed file operations (read, write, append, delete, list) for LangGraph agents and MCP clients. Features async support, YAML configuration, comprehensive logging, and path traversal protection.
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    Generates DeepWiki-style documentation for private code repositories with RAG-based Q\&A capabilities, semantic code search, and multi-language AST parsing. Supports local LLMs (Ollama) or cloud providers for privacy-focused codebase analysis.
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    MIT
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    A local MCP server that wraps the Obsidian CLI to give AI assistants direct access to read, edit, and manage notes within an Obsidian vault. It enables advanced operations such as frontmatter property management, context-aware searching, and the execution of internal Obsidian commands.
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    A local-first MCP server for generating production-ready React UI components entirely offline without requiring API keys or configuration. It provides tools to search, list, and create over 30 types of components including buttons, forms, and layout elements.
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
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    Apache 2.0