Anthropic를 위해 구축된 141개의 MCP를 찾아보세요.
Facilitates rapid research and development using AI, AI Agents, and Large Language Models within a VS Code environment.
Facilitates context management, todo persistence, and AI second opinions for Claude Code users, enhancing workflow continuity and productivity.
Automates technological watch using Anthropic's Model Context Protocol, Claude Desktop, and a Streamlit dashboard.
Enables LLMs to improve reasoning by providing a 'think' tool that allows for iterative thought processing and scratchpad functionality.
Orchestrates a swarm of specialized Claude 3.7 Sonnet instances to generate optimally coherent responses through ensemble intelligence.
Analyzes system log files using AI to identify errors and warnings, and recommend fixes.
Provides AI agents with professional development guidelines, coding standards, and best practices for writing production-quality code.
Provides a command-line interface for interactive chat with AI models, supporting document retrieval and custom commands.
Facilitates interactive chat with AI models, integrating document retrieval, command-based prompts, and extensible tools via the Model Context Protocol.
Provides filesystem capabilities to Claude, allowing it to read, write, and manipulate files on your system.
Automates posting tweets from X (Twitter) using data sourced from a Google Sheet.
Enables interactive command-line conversations with Anthropic's Claude, supporting document retrieval and custom command execution via the Model Control Protocol.
Enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP).
Simplifies local orchestration by running coding agents directly in your workspace, enhancing developer productivity without complex setups.
Facilitates dynamic and reflective problem-solving through a structured, step-by-step thinking process for AI models.
Enables interactive command-line chat with AI models via the Anthropic API.
Engage with Claude-based AI models through a command-line interface, integrating document referencing and custom command execution.
Enables AI assistants to intelligently read and process both scanned and digital PDF documents using integrated Optical Character Recognition (OCR) and a robust caching system.
Intelligently routes tasks to multiple AI agents based on their strengths or user preferences, with auto-fallback on failure.
Monitor Cursor Pro usage limits and API quotas across various AI services to prevent unexpected charges.
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