[{"_1":2,"_289":-5,"_290":-5},"loaderData",{"_3":4,"_32":33,"_34":35,"_238":239},"root",{"_5":6,"_17":18},"toasts",[7,14],{"_8":9,"_10":11,"_12":13},"uid","01972608-ed17-753d-b14b-dc9aae2baf8f","title","This MCP server does not support inspection","type","error",{"_8":15,"_10":16,"_12":13},"01972608-f645-704e-9cfc-f1375c040e45","You need to sign in to access this page","visitor",{"_19":20,"_21":22},"viewportWidth",1280,"visitorSession",{"_23":24,"_26":27,"_28":-5,"_29":30,"_31":-5},"attributes",[25],"BOT","id",7058884,"membership","referenceId","01971406-43e3-74e8-b880-2f000258007a","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_36":37,"_78":-5,"_79":38,"_39":80,"_157":158,"_237":133},"availableLocales",[38,50,57,64,71],{"_39":40,"_43":47,"_48":49},"mcpServer",{"_41":42,"_43":44,"_45":46},"description","A server implementation of the Model Context Protocol that enables calling iFlytek workflows, supporting complex orchestration modes like sequential, parallel, and looped execution across 14 node types for diverse business scenarios.","name","iFlytek Workflow MCP Server","readme","

\n \n

\n The fastest way to build workflows with an AI agent platform!\n

\n \n $\"License\"$ \n |\n \n Docs\n |\n \n Homepage\n \n

\n\n# iFlytek Workflow MCP Server\n\n[The Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol designed for effortless integration between LLM applications and external data sources or tools, offering a standardized framework to seamlessly provide LLMs with the context they require.\n\nThis a simple implementation of an MCP server using iFlytek. It enables calling iFlytek workflows through MCP tools.\n\n## Features\n\n### Functional Overview\n\nThis system is built on the iFlytek MCP server and enables intelligent workflow scheduling, making it suitable for various business scenarios.\n\n- **Workflow Structure**: Composed of multiple nodes, supporting 14 types of nodes (including basic, tool, logic, and transformation types).\n- **Core Components**: By default, the workflow includes a **Start Node** (user input) and an **End Node** (output result).\n- **Execution Mode**: Once triggered, the workflow executes automatically according to predefined sequences and rules, requiring no manual intervention.\n\n### Core Capabilities\n\n#### **Robust Node Support**\n\n- 14 types of workflow nodes to meet diverse business requirements.\n- Supports **complex variable I/O**, enabling flexible data transmission.\n\n#### **Advanced Orchestration Modes**\n\n- **Sequential Execution**: Tasks execute one after another in order.\n- **Parallel Execution**: Multiple tasks run simultaneously to enhance efficiency.\n- **Loop Execution**: Supports iterative loops for handling repetitive tasks.\n- **Nested Execution**: Allows embedding sub-workflows within workflows, improving reusability.\n- Utilizes the **Hook Mechanism** to enable **streaming output**, ensuring real-time processing.\n\n#### **Multiple Development Paradigms**\n\n- **Single-turn, single-branch**: Linear execution of simple tasks.\n- **Single-turn, multi-branch**: Supports branching logic to handle complex processes.\n- **Single-turn loop**: Manages looped tasks to enhance automation.\n- **Multi-turn interaction**: Supports context memory for dynamic conversations.\n\n### Capability Expansion\n\n- **Multi-Model Support**: Based on the **Model of Models (MoM)** hybrid application architecture, providing multiple model choices at critical workflow stages. This allows for flexible model combinations, improving task adaptability.\n\n\n\n## Usage with MCP client\n\n### Prepare config.yaml\n\nBefore using the mcp server, you should prepare a config.yaml to save your workflow info. The example config like this:\n\n```yaml\n- flow_id: 'flow id' # required\n name: 'flow name' # optional, if not set, obtain the name from the cloud.\n description: 'flow description' # optional, if not set, obtain the description from the cloud.\n api_key: 'API Key:API Secret' # required\n```\n\n#### Get workflow authentication information\n1. [Create a bot](https://xinghuo.xfyun.cn/botcenter/createbot)\n![](./images/create_workflow.png)\n\n2. Publish a workflow\n- **Step 1.** Debug the workflow you just created.\n- **Step 2.** Engage in a conversation with your workflow and ensure the conversation is successful.\n- **Step 3.** You can now click the publish button.\n![](./images/debug_workflow.png)\n- **Step 4.** Select \"Publish as API\" and click the \"Configure\" button.\n![](./images/publish_workflow.png)\n- **Step 5.** Select the application you need to bind and bind it. Now you can retrieve the corresponding workflow ID and authentication information. Enjoy!\n![](./images/bind_app.png)\n> **Note**: If you find that you are unable to select an app, you can go to https://www.xfyun.cn to apply.\n### Manual Installation\n\nTo add a persistent client, add the following to your `claude_desktop_config.json` or `mcp.json` file:\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"$CONFIG_PATH\"\n }\n }\n }\n}\n```\n\n\n\nExample config:\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"/Users/hygao1024/Projects/config.yaml\"\n }\n }\n }\n}\n```\n\n\n","English","tag","en-US",{"_39":51,"_43":55,"_48":56},{"_41":52,"_43":53,"_45":54},"Una implementación de servidor del Protocolo de contexto de modelo que permite llamar a flujos de trabajo de iFlytek y admite modos de orquestación complejos como ejecución secuencial, paralela y en bucle en 14 tipos de nodos para diversos escenarios comerciales.","Servidor MCP de flujo de trabajo de iFlytek","# Servidor MCP de flujo de trabajo de iFlytek\n\n[El Protocolo de Contexto de Modelo (MCP)](https://modelcontextprotocol.io/introduction) es un protocolo abierto diseñado para una integración sin esfuerzo entre aplicaciones LLM y herramientas o fuentes de datos externas, y ofrece un marco estandarizado para proporcionar sin problemas a las aplicaciones LLM el contexto que necesitan.\n\nEsta es una implementación sencilla de un servidor MCP con iFlytek. Permite llamar a flujos de trabajo de iFlytek mediante herramientas MCP.\n\n## Características\n\n### Descripción general funcional\n\nEste sistema está construido sobre el servidor iFlytek MCP y permite la programación inteligente del flujo de trabajo, lo que lo hace adecuado para diversos escenarios comerciales.\n\n* **Estructura del flujo de trabajo** : compuesta por varios nodos, que admiten 14 tipos de nodos (incluidos los tipos básicos, de herramientas, lógicos y de transformación).\n* **Componentes principales** : de forma predeterminada, el flujo de trabajo incluye un **nodo de inicio** (entrada del usuario) y un **nodo final** (resultado de salida).\n* **Modo de ejecución** : una vez activado, el flujo de trabajo se ejecuta automáticamente según secuencias y reglas predefinidas, sin necesidad de intervención manual.\n\n### Capacidades principales\n\n#### **Soporte robusto de nodos**\n\n* 14 tipos de nodos de flujo de trabajo para satisfacer diversos requisitos comerciales.\n* Admite **E/S de variables complejas** , lo que permite una transmisión de datos flexible.\n\n#### **Modos de orquestación avanzados**\n\n* **Ejecución secuencial** : las tareas se ejecutan una tras otra en orden.\n* **Ejecución paralela** : varias tareas se ejecutan simultáneamente para mejorar la eficiencia.\n* **Ejecución de bucle** : admite bucles iterativos para gestionar tareas repetitivas.\n* **Ejecución anidada** : permite incorporar subflujos de trabajo dentro de flujos de trabajo, lo que mejora la reutilización.\n* Utiliza el **mecanismo de gancho** para habilitar **la salida de transmisión** , lo que garantiza el procesamiento en tiempo real.\n\n#### **Paradigmas de desarrollo múltiple**\n\n* **Una sola vuelta, una sola rama** : ejecución lineal de tareas simples.\n* **Vuelta única, múltiples ramas** : admite lógica de ramificación para manejar procesos complejos.\n* **Bucle de una sola vuelta** : gestiona tareas en bucle para mejorar la automatización.\n* **Interacción multiturno** : admite memoria de contexto para conversaciones dinámicas.\n\n### Expansión de capacidad\n\n* **Compatibilidad con múltiples modelos** : Basado en la arquitectura de aplicación híbrida del **Modelo de Modelos (MoM)** , ofrece múltiples opciones de modelos en etapas críticas del flujo de trabajo. Esto permite combinaciones flexibles de modelos, mejorando la adaptabilidad de las tareas.\n\n## Uso con el cliente MCP\n\n### Preparar config.yaml\n\nAntes de usar el servidor mcp, debe preparar un archivo config.yaml para guardar la información de su flujo de trabajo. El ejemplo de configuración es el siguiente:\n\n```yaml\n- flow_id: 'flow id' # required\n name: 'flow name' # optional, if not set, obtain the name from the cloud.\n description: 'flow description' # optional, if not set, obtain the description from the cloud.\n api_key: 'API Key:API Secret' # required\n```\n\n#### Obtener información de autenticación del flujo de trabajo\n\n1. [Crear un bot](https://xinghuo.xfyun.cn/botcenter/createbot)![](./images/create_workflow.png)\n\n2. Publicar un flujo de trabajo\n\n* **Paso 1.** Depure el flujo de trabajo que acaba de crear.\n* **Paso 2.** Inicie una conversación con su flujo de trabajo y asegúrese de que la conversación sea exitosa.\n* **Paso 3.** Ahora puedes hacer clic en el botón publicar.![](./images/debug_workflow.png)\n* **Paso 4.** Seleccione \"Publicar como API\" y haga clic en el botón \"Configurar\".![](./images/publish_workflow.png)\n* **Paso 5.** Seleccione la aplicación que necesita vincular y vincúlela. Ahora puede recuperar el ID de flujo de trabajo y la información de autenticación correspondientes. ¡Disfrute!![](./images/bind_app.png)\n\n> **Nota** : Si no puede seleccionar una aplicación, puede ir a para solicitarla.\n\n### Instalación manual\n\nPara agregar un cliente persistente, agregue lo siguiente a su archivo `claude_desktop_config.json` o `mcp.json` :\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"$CONFIG_PATH\"\n }\n }\n }\n}\n```\n\nEjemplo de configuración:\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"/Users/hygao1024/Projects/config.yaml\"\n }\n }\n }\n}\n```","Spanish","es-ES",{"_39":58,"_43":62,"_48":63},{"_41":59,"_43":60,"_45":61},"モデルコンテキストプロトコルのサーバー実装。iFlytek ワークフローの呼び出しを可能にし、多様なビジネスシナリオに対応する 14 種類のノードタイプにわたる順次実行、並列実行、ループ実行などの複雑なオーケストレーションモードをサポートします。","iFlytek ワークフロー MCP サーバー","# iFlytek ワークフロー MCP サーバー\n\n[モデルコンテキストプロトコル (MCP)](https://modelcontextprotocol.io/introduction)は、LLM アプリケーションと外部データソースまたはツール間の簡単な統合のために設計されたオープンプロトコルであり、LLM に必要なコンテキストをシームレスに提供するための標準化されたフレームワークを提供します。\n\nこれはiFlytekを使用したMCPサーバーのシンプルな実装です。MCPツールを介してiFlytekワークフローを呼び出すことができます。\n\n## 特徴\n\n### 機能概要\n\nこのシステムは iFlytek MCP サーバー上に構築されており、インテリジェントなワークフロースケジューリングを可能にするため、さまざまなビジネスシナリオに適しています。\n\n* **ワークフロー構造**: 複数のノードで構成され、14 種類のノード (基本、ツール、ロジック、変換タイプなど) をサポートします。\n* **コアコンポーネント**: デフォルトでは、ワークフローには**開始ノード**(ユーザー入力) と**終了ノード**(出力結果) が含まれます。\n* **実行モード**: ワークフローはトリガーされると、事前定義されたシーケンスとルールに従って自動的に実行されるため、手動による介入は必要ありません。\n\n### コア機能\n\n#### **堅牢なノードサポート**\n\n* 多様なビジネス要件を満たす 14 種類のワークフローノード。\n* **複雑な変数I/O**をサポートし、柔軟なデータ転送を可能にします。\n\n#### **高度なオーケストレーションモード**\n\n* **順次実行**: タスクは順番に次々に実行されます。\n* **並列実行**: 複数のタスクを同時に実行して効率を高めます。\n* **ループ実行**: 繰り返しタスクを処理するための反復ループをサポートします。\n* **ネストされた実行**: ワークフロー内にサブワークフローを埋め込むことができ、再利用性が向上します。\n* **フックメカニズム**を利用して**ストリーミング出力**を可能にし、リアルタイム処理を保証します。\n\n#### **複数の開発パラダイム**\n\n* **シングルターン、シングルブランチ**: 単純なタスクを線形に実行します。\n* **シングルターン、マルチブランチ**: 複雑なプロセスを処理するための分岐ロジックをサポートします。\n* **シングルターンループ**: ループされたタスクを管理して自動化を強化します。\n* **マルチターンインタラクション**: 動的な会話のコンテキストメモリをサポートします。\n\n### 能力拡張\n\n* **マルチモデルサポート**： \\*\\*Model of Models（MoM）\\*\\*ハイブリッドアプリケーションアーキテクチャに基づき、ワークフローの重要な段階で複数のモデルを選択できるようにします。これにより、柔軟なモデルの組み合わせが可能になり、タスクの適応性が向上します。\n\n## MCPクライアントでの使用\n\n### config.yamlを準備する\n\nmcpサーバーを使用する前に、ワークフロー情報を保存するconfig.yamlを準備する必要があります。設定例は以下のとおりです。\n\n```yaml\n- flow_id: 'flow id' # required\n name: 'flow name' # optional, if not set, obtain the name from the cloud.\n description: 'flow description' # optional, if not set, obtain the description from the cloud.\n api_key: 'API Key:API Secret' # required\n```\n\n#### ワークフロー認証情報を取得する\n\n1. [ボットを作成する](https://xinghuo.xfyun.cn/botcenter/createbot)![](./images/create_workflow.png)\n\n2. ワークフローを公開する\n\n* \\*\\*ステップ 1.\\*\\*作成したワークフローをデバッグします。\n* \\*\\*ステップ 2.\\*\\*ワークフローとの会話に参加し、会話が成功することを確認します。\n* \\*\\*ステップ 3.\\*\\*これで公開ボタンをクリックできるようになります。![](./images/debug_workflow.png)\n* **ステップ 4.** 「API として公開」を選択し、「構成」ボタンをクリックします。![](./images/publish_workflow.png)\n* \\*\\*ステップ5.\\*\\*バインドするアプリケーションを選択し、バインドします。これで、対応するワークフローIDと認証情報を取得できます。どうぞお楽しみください！![](./images/bind_app.png)\n\n> **注**: アプリを選択できない場合は、にアクセスして申請してください。\n\n### 手動インストール\n\n永続クライアントを追加するには、 `claude_desktop_config.json`または`mcp.json`ファイルに次の内容を追加します。\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"$CONFIG_PATH\"\n }\n }\n }\n}\n```\n\n設定例:\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"/Users/hygao1024/Projects/config.yaml\"\n }\n }\n }\n}\n```","Japanese","ja-JP",{"_39":65,"_43":69,"_48":70},{"_41":66,"_43":67,"_45":68},"다양한 비즈니스 시나리오에서 14개 노드 유형에 걸쳐 순차, 병렬, 루프 실행과 같은 복잡한 오케스트레이션 모드를 지원하고 iFlytek 워크플로우를 호출할 수 있는 모델 컨텍스트 프로토콜의 서버 구현입니다.","iFlytek 워크플로 MCP 서버","# iFlytek 워크플로 MCP 서버\n\n[모델 컨텍스트 프로토콜(MCP)](https://modelcontextprotocol.io/introduction) 은 LLM 애플리케이션과 외부 데이터 소스 또는 도구 간의 원활한 통합을 위해 설계된 개방형 프로토콜로, LLM에 필요한 컨텍스트를 원활하게 제공하기 위한 표준화된 프레임워크를 제공합니다.\n\niFlytek을 이용한 MCP 서버의 간단한 구현입니다. MCP 도구를 통해 iFlytek 워크플로를 호출할 수 있습니다.\n\n## 특징\n\n### 기능 개요\n\n이 시스템은 iFlytek MCP 서버 기반으로 구축되었으며 지능형 워크플로 스케줄링을 지원하여 다양한 비즈니스 시나리오에 적합합니다.\n\n* **워크플로우 구조** : 여러 노드로 구성되어 있으며, 기본, 도구, 논리, 변환 유형 등 14가지 유형의 노드를 지원합니다.\n* **핵심 구성 요소** : 기본적으로 워크플로에는 **시작 노드** (사용자 입력)와 **종료 노드** (출력 결과)가 포함됩니다.\n* **실행 모드** : 워크플로는 트리거되면 사전 정의된 순서와 규칙에 따라 자동으로 실행되므로 수동 개입이 필요하지 않습니다.\n\n### 핵심 역량\n\n#### **강력한 노드 지원**\n\n* 다양한 비즈니스 요구 사항을 충족하는 14가지 유형의 워크플로 노드.\n* **복잡한 가변 I/O를** 지원하여 유연한 데이터 전송이 가능합니다.\n\n#### **고급 오케스트레이션 모드**\n\n* **순차적 실행** : 작업이 순서대로 하나씩 실행됩니다.\n* **병렬 실행** : 여러 작업을 동시에 실행하여 효율성을 높입니다.\n* **루프 실행** : 반복적인 작업을 처리하기 위한 반복 루프를 지원합니다.\n* **중첩 실행** : 워크플로 내에 하위 워크플로를 내장하여 재사용성을 향상시킵니다.\n* **Hook Mechanism을** 활용하여 **스트리밍 출력을** 활성화하고 실시간 처리를 보장합니다.\n\n#### **다양한 개발 패러다임**\n\n* **단일 턴, 단일 브랜치** : 간단한 작업의 선형 실행.\n* **단일 턴, 다중 분기** : 복잡한 프로세스를 처리하기 위한 분기 논리를 지원합니다.\n* **단일 턴 루프** : 루프 작업을 관리하여 자동화를 강화합니다.\n* **멀티턴 상호작용** : 동적 대화를 위한 컨텍스트 메모리를 지원합니다.\n\n### 역량 확장\n\n* **다중 모델 지원** : **MoM(Model of Models)** 하이브리드 애플리케이션 아키텍처를 기반으로 주요 워크플로 단계에서 다양한 모델 선택권을 제공합니다. 이를 통해 유연한 모델 조합이 가능해져 작업 적응성이 향상됩니다.\n\n## MCP 클라이언트와 함께 사용\n\n### config.yaml을 준비하세요\n\nMCP 서버를 사용하기 전에 워크플로 정보를 저장할 config.yaml 파일을 준비해야 합니다. 예제 구성은 다음과 같습니다.\n\n지엑스피1\n\n#### 워크플로 인증 정보 가져오기\n\n1. [봇 만들기](https://xinghuo.xfyun.cn/botcenter/createbot)![](./images/create_workflow.png)\n\n2. 워크플로 게시\n\n* **1단계.** 방금 만든 워크플로를 디버깅합니다.\n* **2단계.** 워크플로와 대화를 시작하고 대화가 성공적으로 이루어지도록 하세요.\n* **3단계.** 이제 게시 버튼을 클릭할 수 있습니다.![](./images/debug_workflow.png)\n* **4단계.** \"API로 게시\"를 선택하고 \"구성\" 버튼을 클릭합니다.![](./images/publish_workflow.png)\n* **5단계.** 바인딩할 애플리케이션을 선택하고 바인딩하세요. 이제 해당 워크플로 ID와 인증 정보를 가져올 수 있습니다. 즐겨보세요!![](./images/bind_app.png)\n\n> **참고** : 앱을 선택할 수 없는 경우 으로 이동하여 신청하세요.\n\n### 수동 설치\n\n영구 클라이언트를 추가하려면 `claude_desktop_config.json` 또는 `mcp.json` 파일에 다음을 추가하세요.\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"$CONFIG_PATH\"\n }\n }\n }\n}\n```\n\n구성 예:\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"/Users/hygao1024/Projects/config.yaml\"\n }\n }\n }\n}\n```","Korean","ko-KR",{"_39":72,"_43":76,"_48":77},{"_41":73,"_43":74,"_45":75},"模型上下文协议的服务端实现，支持调用科大讯飞工作流，支持14种节点类型的顺序、并行、循环执行等复杂编排模式，满足多样化的业务场景。","科大讯飞工作流MCP服务器","# 科大讯飞工作流MCP服务器\n\n[模型上下文协议 (MCP)](https://modelcontextprotocol.io/introduction)是一种开放协议，旨在实现 LLM 应用程序与外部数据源或工具之间的轻松集成，提供标准化框架，无缝地为 LLM 提供所需的上下文。\n\n这是一个使用科大讯飞 (iFlytek) 实现的 MCP 服务器的简单示例。它支持通过 MCP 工具调用科大讯飞工作流。\n\n## 特征\n\n### 功能概述\n\n该系统基于科大讯飞MCP服务器构建，实现工作流智能调度，适用于各种业务场景。\n\n* **工作流结构**：由多个节点组成，支持14种节点类型（包括基本、工具、逻辑和转换类型）。\n* **核心组件**：默认情况下，工作流包括一个**起始节点**（用户输入）和一个**结束节点**（输出结果）。\n* **执行模式**：一旦触发，工作流将按照预先定义的顺序和规则自动执行，无需人工干预。\n\n### 核心能力\n\n#### **强大的节点支持**\n\n* 14种工作流节点，满足多样化的业务需求。\n* 支持**复杂变量I/O** ，实现灵活的数据传输。\n\n#### **高级编排模式**\n\n* **顺序执行**：任务按顺序一个接一个地执行。\n* **并行执行**：多个任务同时运行以提高效率。\n* **循环执行**：支持迭代循环来处理重复任务。\n* **嵌套执行**：允许在工作流中嵌入子工作流，提高可重用性。\n* 利用**Hook机制**实现**流式输出**，保证实时处理。\n\n#### **多种开发范式**\n\n* **单回合、单分支**：简单任务的线性执行。\n* **单转多分支**：支持分支逻辑，处理复杂流程。\n* **单圈循环**：管理循环任务以增强自动化。\n* **多轮互动**：支持动态对话的上下文记忆。\n\n### 能力扩展\n\n* **多模型支持**：基于\\*\\*模型之模型 (MoM)\\*\\*混合应用架构，在关键工作流阶段提供多种模型选择。这允许灵活的模型组合，从而提高任务适应性。\n\n## 与 MCP 客户端一起使用\n\n### 准备 config.yaml\n\n在使用 mcp 服务器之前，你需要准备一个 config.yaml 文件来保存你的工作流程信息。示例配置如下：\n\n```yaml\n- flow_id: 'flow id' # required\n name: 'flow name' # optional, if not set, obtain the name from the cloud.\n description: 'flow description' # optional, if not set, obtain the description from the cloud.\n api_key: 'API Key:API Secret' # required\n```\n\n#### 获取工作流身份验证信息\n\n1. [创建机器人](https://xinghuo.xfyun.cn/botcenter/createbot)![](./images/create_workflow.png)\n\n2. 发布工作流\n\n* \\*\\*步骤 1.\\*\\*调试您刚刚创建的工作流。\n* \\*\\*第 2 步。\\*\\*与您的工作流程进行对话并确保对话成功。\n* \\*\\*步骤3.\\*\\*您现在可以单击发布按钮。![](./images/debug_workflow.png)\n* \\*\\*步骤4.\\*\\*选择“发布为API”，点击“配置”按钮。![](./images/publish_workflow.png)\n* \\*\\*步骤5.\\*\\*选择您需要绑定的应用程序并进行绑定。现在您可以检索相应的工作流ID和身份验证信息。尽情享受吧！![](./images/bind_app.png)\n\n> **注**：如您发现无法选择应用，您可以前往进行申请。\n\n### 手动安装\n\n要添加持久客户端，请将以下内容添加到您的`claude_desktop_config.json`或`mcp.json`文件中：\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n \"ifly_workflow_mcp_server\"\n ],\n \"env\": {\n \"CONFIG_PATH\": \"$CONFIG_PATH\"\n }\n }\n }\n}\n```\n\n示例配置：\n\n```json\n{\n \"mcpServers\": {\n \"ifly-workflow-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\n \"--from\",\n \"git+https://github.com/iflytek/ifly-workflow-mcp-server\",\n 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