OneAIをChatGPT、Claude、Geminiに接続

• 01 / 03感情分析の方法
• 02 / 03エンティティの抽出方法
• 03 / 03音声の書き起こし方法

最速日

790ms

最遅日

1006ms

14日間の傾向

遅延傾向+27%

OneAI Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_OUuZaJpMXxYZjYxd7ELA63wi1sMTV0hm1c50LuC2/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — OneAI capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "oneai-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_OUuZaJpMXxYZjYxd7ELA63wi1sMTV0hm1c50LuC2/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ OneAIをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  コンテンツクリエイター

  長い記事の自動要約や、ポッドキャストのエピソードの文字起こしに活用できます。

• 02

  データアナリスト

  顧客のフィードバックやメールから、構造化されたエンティティや感情を抽出できます。

• 03

  開発者

  カスタムモデルを自作することなく、高度なNLP機能をワークフローに統合できます。

• 01

  How do I use OneAI with Claude?

  You connect the OneAI MCP through Vinkius. Once connected, Claude can call the capabilities in this MCP to process text and media.

• 02

  Can I process large video files?

  Yes. You use the run_async_pipeline capability to handle large files or audio that require more time to process.

• 03

  Do I need to host the MCP myself?

  No. Vinkius hosts and manages the MCP for you, so it's ready to use immediately after you connect your API key.

• 04

  How do I know when a large task is finished?

  You can use the get_async_task_status capability to check the progress of any asynchronous pipeline task.

• 05

  What kind of text analysis is available?

  The MCP supports summarization, sentiment detection, and entity extraction through its synchronous pipelines.

• 06

  How can I summarize a text and extract entities at the same time?

  Use the run_pipeline capability. Provide your text in the input field and define the steps as a JSON array like [{"skill":"summarize"}, {"skill":"entities"}].

• 07

  What should I use for processing large audio files?

  Use run_async_pipeline. You can provide a content_url for the audio file and set the steps to include the transcribe skill. This starts a background task.

• 08

  How do I know when my asynchronous processing is finished?

  Use the get_async_task_status capability with the task_id returned by the async pipeline. It will provide the current status and the final results once completed.

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