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MCP Model Context Protocol
Original title: MCP Model Context Protocol
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MCP (Model Context Protocol) is an open standard technology announced by Anthropic that seamlessly connects AI models (LLMs) with external data and tools. It standardizes connections between different AI tools and databases like “USB-C” and is attracting attention as a technology dramatically improving the efficiency of building AI agents and automating work.
Standardized integration: Removes the need for individual development previously required for each API, connecting various tools (Slack, GitHub, databases, etc.) to AI through common procedures.
Productivity improvement: AI becomes able to obtain and manipulate real-time external information, making development of agents specializing in particular tasks easier.
AI’s “USB-C”: once a tool is created to the MCP standard, any AI model (Claude, ChatGPT, etc.) can use it.
Components of MCP
MCP host: an AI application interacting with the user (example: Claude for Desktop).
MCP client: intermediary between host and server.
MCP server: Connectivity to data sources and tools.
MCP(Model Context Protocol)は、Anthropicが発表した、AIモデル(LLM)と外部データやツールをシームレスに繋ぐオープン標準技術です。異なるAIツールやデータベース間の連携を「USB-C」のように共通化し、AIエージェントの構築や業務自動化の効率を劇的に向上させる技術として注目されています。
連携の標準化: 従来、APIごとに必要だった個別開発を不要にし、共通の手順で様々なツール(Slack, GitHub, データベース等)をAIに接続。
生産性向上: AIがリアルタイムの外部情報を取得・操作できるようになり、特定の業務に特化したエージェント開発が容易になる。
AIの「USB-C」: 一度MCP規格でツールを作れば、どのAIモデル(Claude、ChatGPTなど)からも利用可能。
MCPの構成要素
MCPホスト: ユーザーと対話するAIアプリケーション(例: Claude for Desktop)。
MCPクライアント: ホストとサーバーの仲介役。
MCPサーバー: データソースやツールへの接続機能。
Source updated 2026-04-28 · Snapshot 2026-10-08
Source links and calculated neighbors
Cosine values measure shared lexical features, not truth, agreement or identical meaning. Original reference links are labeled separately.
- ISMIR MIDI DatabaseComputed lexical cosine similarity 0.099 · shared title, text, tags and references
- Ableton MCP: External ControlComputed lexical cosine similarity 0.090 · shared title, text, tags and references
- SARSAComputed lexical cosine similarity 0.086 · shared title, text, tags and references