ChainlitをChatGPT、Claude、Geminiに接続

• 01 / 03ユーザーフィードバックの分析方法
• 02 / 03AIモデルの失敗原因を特定する
• 03 / 03プロジェクトの利用状況を監視する方法

最速日

748ms

最遅日

1093ms

14日間の傾向

遅延傾向+18%

Chainlit 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_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/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 — Chainlit capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "chainlit-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/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？ Chainlit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

  AIデベロッパー

  本番環境でのモデルの失敗原因を、使用された論理シーケンスやパラメータスタックから即座に診断します。

• 02

  プロダクトチーム

  ポジティブなフィードバックとネガティブな結果の比率を監視し、LLMに最悪のチャット内容を自動要約させます。

• 03

  QAスペシャリスト

  膨大なログを手動で読むことなく、会話のトーン、関連性、コンプライアンス基準を定期的に自動評価します。

• 01

  How do I connect my Chainlit projects to this MCP?

  You subscribe via Vinkius and then provide your Chainlit Cloud URL and Project API Key. Once connected, your AI client can immediately start querying your data.

• 02

  Which AI clients can I use with this MCP?

  You can use any MCP-compatible client, including Claude, Cursor, and Windsurf.

• 03

  Can I see the specific prompts my model used?

  Yes. By using the list_steps capability, your agent can retrieve the exact prompts and generations used within a specific conversation thread.

• 04

  Is my data secure?

  Yes. Vinkius hosts the MCP and manages your credentials securely, allowing your agent to interact with your Chainlit Cloud projects safely.

• 05

  Can I use this to monitor user satisfaction?

  Yes. The list_feedbacks capability allows your agent to pull user thumbs-up/down ratings and textual reviews directly.

• 06

  Will the AI agent be able to monitor the user interactions and evaluate chat history?

  Yes! The agent can dive into the list_threads and get_thread endpoints to retrieve comprehensive interaction logs from your deployed Chainlit apps. You can essentially command the agent to read past AI chats, summarize usage, or identify edge cases in the user input.

• 07

  Can it track the individual thought steps and LLM prompt tokens consumed?

  Absolutely. Using the list_steps capability, your agent analyzes the programmatic trace—including specific LLM calls, function blocks, or retrieval events. Thus, identifying hallucinations or latency issues is as easy as typing a prompt.

• 08

  Is it possible to extract and analyze human feedback scores instantly?

  Yes. The integration provides native capabilities via list_feedbacks to retrieve the explicit thumbs up, down, and textual comments your users left on specific messages, streamlining QA.

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