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"Assistance with CURSOR Programming" matching MCP servers:

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    An MCP server that lets Claude spawn Cursor subagents as async, bounded workers, enabling non-blocking task delegation with asynchronous completion notifications.
    8
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    Enables interactive feedback with image support in Cursor, allowing users to paste images for visual context and feedback.
    1
    8
    MIT
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    Enables fast literal and regex search over local workspaces using a sparse n-gram index for candidate pruning, with deterministic verification against file contents.
    8
    1
    MIT
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    Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.
    2
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    An MCP server that integrates Alibaba's DashScope platform, providing AI chat tools and resource access through both standard stdio and a web-based interface. It allows users to interact with DashScope large models within the Model Context Protocol ecosystem using TypeScript.
    1
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    A FastMCP server providing hackathon overview resources and reusable starter prompts for quick integration with Cursor.
    1
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    Enables querying weather forecasts for Israeli cities by automating browser with Playwright, extracting data from Weather2Day website, and feeding it to an LLM for natural language responses.
    4
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    Connects Google Analytics 4 data to Claude, Cursor and other MCP clients, enabling natural language queries of website traffic, user behavior, and analytics data with access to 200+ GA4 dimensions and metrics.
    10
    235
    Apache 2.0
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    Provides fast, token-efficient search over coding agent documentation (e.g., Claude Code, Cursor) using local SQLite FTS5 indexing, with tools for searching snippets, reading pages, and grepping markdown.
    5
    MIT
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    Enables natural language querying of SQL databases by allowing the agent to explore schema, write SQL, and self-correct errors. It integrates with MCP-compatible assistants like Claude Desktop or Cursor.
    1
    1
    MIT
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    An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.
    11
    19,534
    19,859
    Elastic 2.0