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

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    A production-grade MCP server for the Code-Fundi API, enabling AI agents to map codebases, search semantically, and analyze blast radius. It provides tools for repository management, AI-powered research, and impact analysis before shipping changes.
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    MIT
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    An MCP server that connects AI assistants to a Neo4j graph database, enabling querying of movie data through tools like graph statistics, genre-based search, and movie details, along with advanced features such as LLM sampling and completions.
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    Enables AI agents to directly control Figma Desktop via MCP, supporting UI creation, editing, prototyping, and variable management with over 60 tools.
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    MIT
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    Enables local AI agents to generate and edit Figma designs via the Plugin API through a WebSocket relay, with no rate limits and all communication remaining local.
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    Provides comprehensive filesystem operations, text search/replace, image processing, and Tesseract OCR capabilities for AI agents via the Model Context Protocol.
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    Apache 2.0
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    Enables LLMs to interact with the AT Protocol ecosystem, including Bluesky, through natural language. Supports public data access without authentication and full write operations with authentication.
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    MIT
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    A TypeScript-based server that enables programmatic management of pseudo-terminal sessions. It allows users to spawn, control, and interact with terminal processes in real-time through the Model Context Protocol.
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    AGPL 3.0
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    MCP server that injects verified FTC documentation and code examples into AI assistants, enabling teams to write correct, competition-ready Java robot code through natural language.
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    MIT
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    A production-grade TypeScript starter for building Model Context Protocol servers, supporting stdio and Streamable HTTP transports with modular tools, resources, and prompts.
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    MIT
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    DevEyes is a Model Context Protocol (MCP) server that captures screenshots from your local development environment and automatically optimizes them for LLM consumption.
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    MIT
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    A small, readable MCP learning project implemented in TypeScript that uses a local JSON notebook to demonstrate core MCP features such as tools, resources, prompts, sampling, and notifications.
    6
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    A TypeScript-based MCP server that enables users to interact with Prometheus metrics using PromQL queries and discovery tools. It allows LLMs to retrieve time-series data, metadata, alerts, and system status directly from a Prometheus instance.
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    A minimal TypeScript starter template for building Model Context Protocol (MCP) servers with auto-loading architecture for tools, resources, and prompts. Includes code generators, dual transport support (stdio/HTTP), and production-ready structure.
    3