[{"_1":2,"_360":-5,"_361":-5},"loaderData",{"_3":4,"_22":23,"_24":25,"_290":291},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"BOT","id",7689702,"membership","referenceId","0197490c-cedf-735b-bcc0-5effc1b1ee93","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_68":-5,"_69":28,"_29":70,"_206":207,"_288":289},"availableLocales",[28,40,47,54,61],{"_29":30,"_33":37,"_38":39},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","Provides tools for listing and retrieving content from different knowledge bases using semantic search capabilities.","name","Knowledge Base MCP Server","readme","# Knowledge Base MCP Server\n\n[![smithery badge](https://smithery.ai/badge/@jeanibarz/knowledge-base-mcp-server)](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server)\nThis MCP server provides tools for listing and retrieving content from different knowledge bases.\n\n\n $\"Knowledge$ \n\n\n## Setup Instructions\n\nThese instructions assume you have Node.js and npm installed on your system.\n\n### Installing via Smithery\n\nTo install Knowledge Base Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server):\n\n```bash\nnpx -y @smithery/cli install @jeanibarz/knowledge-base-mcp-server --client claude\n```\n\n### Manual Installation\n**Prerequisites**\n\n* [Node.js](https://nodejs.org/) (version 16 or higher)\n* [npm](https://www.npmjs.com/) (Node Package Manager)\n\n1. **Clone the repository:**\n\n ```bash\n git clone \n cd knowledge-base-mcp-server\n ```\n\n2. **Install dependencies:**\n\n ```bash\n npm install\n ```\n\n3. **Configure environment variables:**\n\n * The server requires the `HUGGINGFACE_API_KEY` environment variable to be set. This is the API key for the Hugging Face Inference API, which is used to generate embeddings for the knowledge base content. You can obtain a free API key from the Hugging Face website ([https://huggingface.co/](https://huggingface.co/)).\n * The server requires the `KNOWLEDGE_BASES_ROOT_DIR` environment variable to be set. This variable specifies the directory where the knowledge base subdirectories are located. If you don't set this variable, it will default to `$HOME/knowledge_bases`, where `$HOME` is the current user's home directory.\n * The server supports the `FAISS_INDEX_PATH` environment variable to specify the path to the FAISS index. If not set, it will default to `$HOME/knowledge_bases/.faiss`.\n * The server supports the `HUGGINGFACE_MODEL_NAME` environment variable to specify the Hugging Face model to use for generating embeddings. If not set, it will default to `sentence-transformers/all-MiniLM-L6-v2`.\n * You can set these environment variables in your `.bashrc` or `.zshrc` file, or directly in the MCP settings.\n\n4. **Build the server:**\n\n ```bash\n npm run build\n ```\n\n5. **Add the server to the MCP settings:**\n\n * Edit the `cline_mcp_settings.json` file located at `/home/jean/.vscode-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/`.\n * Add the following configuration to the `mcpServers` object:\n\n ```json\n \"knowledge-base-mcp\": {\n \"command\": \"node\",\n \"args\": [\n \"/path/to/knowledge-base-mcp-server/build/index.js\"\n ],\n \"disabled\": false,\n \"autoApprove\": [],\n \"env\": {\n \"KNOWLEDGE_BASES_ROOT_DIR\": \"/path/to/knowledge_bases\",\n \"HUGGINGFACE_API_KEY\": \"YOUR_HUGGINGFACE_API_KEY\",\n },\n \"description\": \"Retrieves similar chunks from the knowledge base based on a query.\"\n },\n ```\n\n * Replace `/path/to/knowledge-base-mcp-server` with the actual path to the server directory.\n * Replace `/path/to/knowledge_bases` with the actual path to the knowledge bases directory.\n\n6. **Create knowledge base directories:**\n\n * Create subdirectories within the `KNOWLEDGE_BASES_ROOT_DIR` for each knowledge base (e.g., `company`, `it_support`, `onboarding`).\n * Place text files (e.g., `.txt`, `.md`) containing the knowledge base content within these subdirectories.\n\n* The server recursively reads all text files (e.g., `.txt`, `.md`) within the specified knowledge base subdirectories.\n* The server skips hidden files and directories (those starting with a `.`).\n* For each file, the server calculates the SHA256 hash and stores it in a file with the same name in a hidden `.index` subdirectory. This hash is used to determine if the file has been modified since the last indexing.\n* The file content is splitted into chunks using the `MarkdownTextSplitter` from `langchain/text_splitter`.\n* The content of each chunk is then added to a FAISS index, which is used for similarity search.\n* The FAISS index is automatically initialized when the server starts. It checks for changes in the knowledge base files and updates the index accordingly.\n\n## Usage\n\nThe server exposes two tools:\n\n* `list_knowledge_bases`: Lists the available knowledge bases.\n* `retrieve_knowledge`: Retrieves similar chunks from the knowledge base based on a query. Optionally, if a knowledge base is specified, only that one is searched; otherwise, all available knowledge bases are considered. By default, at most 10 document chunks are returned with a score below a threshold of 2. A different threshold can optionally be provided using the `threshold` parameter.\n\nYou can use these tools through the MCP interface.\n\nThe `retrieve_knowledge` tool performs a semantic search using a FAISS index. The index is automatically updated when the server starts or when a file in a knowledge base is modified.\n\nThe output of the `retrieve_knowledge` tool is a markdown formatted string with the following structure:\n\n````markdown\n## Semantic Search Results\n\n**Result 1:**\n\n[Content of the most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n---\n\n**Result 2:**\n\n[Content of the second most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n> **Disclaimer:** The provided results might not all be relevant. Please cross-check the relevance of the information.\n````\n\nEach result includes the content of the most similar chunk, the source file, and a similarity score.\n","English","tag","en-US",{"_29":41,"_33":45,"_38":46},{"_31":42,"_33":43,"_35":44},"Proporciona herramientas para enumerar y recuperar contenido de diferentes bases de conocimiento utilizando capacidades de búsqueda semántica.","Base de conocimientos del servidor MCP","# Base de conocimientos del servidor MCP\n\n[![insignia de herrería](https://smithery.ai/badge/@jeanibarz/knowledge-base-mcp-server)](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server) Este servidor MCP proporciona herramientas para enumerar y recuperar contenido de diferentes bases de conocimiento.\n\n## Instrucciones de configuración\n\nEstas instrucciones asumen que tiene Node.js y npm instalados en su sistema.\n\n### Instalación mediante herrería\n\nPara instalar Knowledge Base Server para Claude Desktop automáticamente a través de [Smithery](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server) :\n\n```bash\nnpx -y @smithery/cli install @jeanibarz/knowledge-base-mcp-server --client claude\n```\n\n### Instalación manual\n\n**Prerrequisitos**\n\n* [Node.js](https://nodejs.org/) (versión 16 o superior)\n* [npm](https://www.npmjs.com/) (Administrador de paquetes de Node)\n\n1. **Clonar el repositorio:**\n\n ```bash\n git clone \n cd knowledge-base-mcp-server\n ```\n\n2. **Instalar dependencias:**\n\n ```bash\n npm install\n ```\n\n3. **Configurar variables de entorno:**\n\n * El servidor requiere que se configure la variable de entorno `HUGGINGFACE_API_KEY` . Esta es la clave API para la API de inferencia de Hugging Face, que se utiliza para generar incrustaciones para el contenido de la base de conocimiento. Puede obtener una clave API gratuita en el sitio web de Hugging Face ( ).\n * El servidor requiere que se configure la variable de entorno `KNOWLEDGE_BASES_ROOT_DIR` . Esta variable especifica el directorio donde se encuentran los subdirectorios de la base de conocimientos. Si no se configura, se tomará por defecto `$HOME/knowledge_bases` , donde `$HOME` es el directorio de inicio del usuario actual.\n * El servidor admite la variable de entorno `FAISS_INDEX_PATH` para especificar la ruta al índice FAISS. Si no se configura, se usará de forma predeterminada `$HOME/knowledge_bases/.faiss` .\n * El servidor admite la variable de entorno `HUGGINGFACE_MODEL_NAME` para especificar el modelo Hugging Face que se usará para generar incrustaciones. Si no se configura, se usará `sentence-transformers/all-MiniLM-L6-v2` de forma predeterminada.\n * Puede configurar estas variables de entorno en su archivo `.bashrc` o `.zshrc` , o directamente en la configuración de MCP.\n\n4. **Construir el servidor:**\n\n ```bash\n npm run build\n ```\n\n5. **Agregue el servidor a la configuración de MCP:**\n\n * Edite el archivo `cline_mcp_settings.json` ubicado en `/home/jean/.vscode-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/` .\n * Agregue la siguiente configuración al objeto `mcpServers` :\n\n ```json\n \"knowledge-base-mcp\": {\n \"command\": \"node\",\n \"args\": [\n \"/path/to/knowledge-base-mcp-server/build/index.js\"\n ],\n \"disabled\": false,\n \"autoApprove\": [],\n \"env\": {\n \"KNOWLEDGE_BASES_ROOT_DIR\": \"/path/to/knowledge_bases\",\n \"HUGGINGFACE_API_KEY\": \"YOUR_HUGGINGFACE_API_KEY\",\n },\n \"description\": \"Retrieves similar chunks from the knowledge base based on a query.\"\n },\n ```\n\n * Reemplace `/path/to/knowledge-base-mcp-server` con la ruta real al directorio del servidor.\n * Reemplace `/path/to/knowledge_bases` con la ruta real al directorio de bases de conocimiento.\n\n6. **Crear directorios de base de conocimientos:**\n\n * Cree subdirectorios dentro de `KNOWLEDGE_BASES_ROOT_DIR` para cada base de conocimiento (por ejemplo, `company` , `it_support` , `onboarding` ).\n * Coloque los archivos de texto (por ejemplo, `.txt` , `.md` ) que contienen el contenido de la base de conocimiento dentro de estos subdirectorios.\n\n* El servidor lee de forma recursiva todos los archivos de texto (por ejemplo, `.txt` , `.md` ) dentro de los subdirectorios de la base de conocimiento especificados.\n* El servidor omite los archivos y directorios ocultos (aquellos que comienzan con `.` ).\n* Para cada archivo, el servidor calcula el hash SHA256 y lo almacena en un archivo con el mismo nombre en un subdirectorio oculto `.index` . Este hash se utiliza para determinar si el archivo se ha modificado desde la última indexación.\n* El contenido del archivo se divide en fragmentos utilizando `MarkdownTextSplitter` de `langchain/text_splitter` .\n* Luego, el contenido de cada fragmento se agrega a un índice FAISS, que se utiliza para la búsqueda de similitud.\n* El índice FAISS se inicializa automáticamente al iniciar el servidor. Comprueba si hay cambios en los archivos de la base de conocimiento y actualiza el índice según corresponda.\n\n## Uso\n\nEl servidor expone dos herramientas:\n\n* `list_knowledge_bases` : enumera las bases de conocimiento disponibles.\n* `retrieve_knowledge` : Recupera fragmentos similares de la base de conocimiento mediante una consulta. Opcionalmente, si se especifica una base de conocimiento, solo se busca en ella; de lo contrario, se consideran todas las bases de conocimiento disponibles. Por defecto, se devuelven un máximo de 10 fragmentos de documento con una puntuación inferior a un umbral de 2. Opcionalmente, se puede especificar un umbral diferente mediante el parámetro \" `threshold` \".\n\nPuede utilizar estas herramientas a través de la interfaz MCP.\n\nLa herramienta `retrieve_knowledge` realiza una búsqueda semántica mediante un índice FAISS. Este índice se actualiza automáticamente al iniciar el servidor o al modificar un archivo de una base de conocimiento.\n\nLa salida de la herramienta `retrieve_knowledge` es una cadena con formato Markdown con la siguiente estructura:\n\n````markdown\n## Semantic Search Results\n\n**Result 1:**\n\n[Content of the most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n---\n\n**Result 2:**\n\n[Content of the second most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n> **Disclaimer:** The provided results might not all be relevant. Please cross-check the relevance of the information.\n````\n\nCada resultado incluye el contenido del fragmento más similar, el archivo de origen y una puntuación de similitud.","Spanish","es-ES",{"_29":48,"_33":52,"_38":53},{"_31":49,"_33":50,"_35":51},"セマンティック検索機能を使用して、さまざまなナレッジベースからコンテンツを一覧表示および取得するためのツールを提供します。","ナレッジベースMCPサーバー","# ナレッジベースMCPサーバー\n\n[![鍛冶屋のバッジ](https://smithery.ai/badge/@jeanibarz/knowledge-base-mcp-server)](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server)この MCP サーバーは、さまざまなナレッジベースからコンテンツを一覧表示および取得するためのツールを提供します。\n\n## セットアップ手順\n\nこれらの手順では、システムに Node.js と npm がインストールされていることを前提としています。\n\n### Smithery経由でインストール\n\n[Smithery](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server)経由で Claude Desktop の Knowledge Base Server を自動的にインストールするには:\n\n```bash\nnpx -y @smithery/cli install @jeanibarz/knowledge-base-mcp-server --client claude\n```\n\n### 手動インストール\n\n**前提条件**\n\n* [Node.js](https://nodejs.org/) (バージョン 16 以上)\n* [npm](https://www.npmjs.com/) (ノードパッケージマネージャー)\n\n1. **リポジトリをクローンします。**\n\n ```bash\n git clone \n cd knowledge-base-mcp-server\n ```\n\n2. **依存関係をインストールします:**\n\n ```bash\n npm install\n ```\n\n3. **環境変数を設定します。**\n\n * サーバーでは、 `HUGGINGFACE_API_KEY`環境変数を設定する必要があります。これは、知識ベースコンテンツの埋め込みを生成するために使用されるHugging Face推論APIのAPIキーです。Hugging Faceのウェブサイト（）から無料のAPIキーを入手できます。\n * サーバーでは、 `KNOWLEDGE_BASES_ROOT_DIR`環境変数を設定する必要があります。この変数は、ナレッジベースのサブディレクトリが配置されているディレクトリを指定します。この変数を設定しない場合、デフォルトで`$HOME/knowledge_bases`が使用されます。ここで、 `$HOME`は現在のユーザーのホームディレクトリです。\n * サーバーは、FAISSインデックスへのパスを指定するための`FAISS_INDEX_PATH`環境変数をサポートしています。設定されていない場合は、デフォルトで`$HOME/knowledge_bases/.faiss`が使用されます。\n * サーバーは、埋め込み生成に使用するHugging Faceモデルを指定するための`HUGGINGFACE_MODEL_NAME`環境変数をサポートしています。設定されていない場合は、デフォルトで`sentence-transformers/all-MiniLM-L6-v2`が使用されます。\n * これらの環境変数は、 `.bashrc`または`.zshrc`ファイルで設定することも、MCP 設定で直接設定することもできます。\n\n4. **サーバーを構築します。**\n\n ```bash\n npm run build\n ```\n\n5. **サーバーを MCP 設定に追加します。**\n\n * `/home/jean/.vscode-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/`にある`cline_mcp_settings.json`ファイルを編集します。\n * `mcpServers`オブジェクトに次の構成を追加します。\n\n ```json\n \"knowledge-base-mcp\": {\n \"command\": \"node\",\n \"args\": [\n \"/path/to/knowledge-base-mcp-server/build/index.js\"\n ],\n \"disabled\": false,\n \"autoApprove\": [],\n \"env\": {\n \"KNOWLEDGE_BASES_ROOT_DIR\": \"/path/to/knowledge_bases\",\n \"HUGGINGFACE_API_KEY\": \"YOUR_HUGGINGFACE_API_KEY\",\n },\n \"description\": \"Retrieves similar chunks from the knowledge base based on a query.\"\n },\n ```\n\n * `/path/to/knowledge-base-mcp-server`サーバーディレクトリへの実際のパスに置き換えます。\n * `/path/to/knowledge_bases`ナレッジベースディレクトリへの実際のパスに置き換えます。\n\n6. **ナレッジベースディレクトリを作成します。**\n\n * 各ナレッジベース (例: `company` 、 `it_support` 、 `onboarding` ) の`KNOWLEDGE_BASES_ROOT_DIR`内にサブディレクトリを作成します。\n * これらのサブディレクトリ内に、ナレッジベースのコンテンツを含むテキストファイル (例: `.txt` 、 `.md` ) を配置します。\n\n* サーバーは、指定されたナレッジベースのサブディレクトリ内のすべてのテキストファイル (例: `.txt` 、 `.md` ) を再帰的に読み取ります。\n* サーバーは隠しファイルとディレクトリ ( `.`で始まるもの) をスキップします。\n* サーバーは各ファイルについてSHA256ハッシュを計算し、隠しサブディレクトリ`.index`内の同名のファイルに保存します。このハッシュは、前回のインデックス作成以降にファイルが変更されたかどうかを判断するために使用されます。\n* ファイルの内容は、 `langchain/text_splitter`の`MarkdownTextSplitter`を使用してチャンクに分割されます。\n* 各チャンクの内容は、類似性検索に使用される FAISS インデックスに追加されます。\n* FAISSインデックスは、サーバーの起動時に自動的に初期化されます。ナレッジベースファイルの変更をチェックし、それに応じてインデックスを更新します。\n\n## 使用法\n\nサーバーは 2 つのツールを公開します。\n\n* `list_knowledge_bases` : 利用可能なナレッジベースを一覧表示します。\n* クエリに基づいてナレッジベースから類似のチャンクを取得し`retrieve_knowledge` 。オプションでナレッジベースを指定した場合は、そのナレッジベースのみが検索されます。指定しない場合は、利用可能なすべてのナレッジベースが対象となります。デフォルトでは、スコアがしきい値2未満のドキュメントチャンクが最大10個返されます。threshold `threshold`を使用して、異なるしきい値を指定することもできます。\n\nこれらのツールは、MCP インターフェースを通じて使用できます。\n\n`retrieve_knowledge`ツールは、FAISSインデックスを使用してセマンティック検索を実行します。インデックスは、サーバーの起動時またはナレッジベース内のファイルが変更されたときに自動的に更新されます。\n\n`retrieve_knowledge`ツールの出力は、次の構造を持つマークダウン形式の文字列です。\n\n````markdown\n## Semantic Search Results\n\n**Result 1:**\n\n[Content of the most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n---\n\n**Result 2:**\n\n[Content of the second most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n> **Disclaimer:** The provided results might not all be relevant. Please cross-check the relevance of the information.\n````\n\n各結果には、最も類似したチャンクのコンテンツ、ソースファイル、および類似度スコアが含まれます。","Japanese","ja-JP",{"_29":55,"_33":59,"_38":60},{"_31":56,"_33":57,"_35":58},"의미 검색 기능을 사용하여 다양한 지식 기반의 콘텐츠를 나열하고 검색하는 도구를 제공합니다.","지식 기반 MCP 서버","# 지식 기반 MCP 서버\n\n[![대장간 배지](https://smithery.ai/badge/@jeanibarz/knowledge-base-mcp-server)](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server) 이 MCP 서버는 다양한 지식 기반의 콘텐츠를 나열하고 검색하는 도구를 제공합니다.\n\n## 설치 지침\n\n이 지침에서는 시스템에 Node.js와 npm이 설치되어 있다고 가정합니다.\n\n### Smithery를 통해 설치\n\n[Smithery를](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server) 통해 Claude Desktop용 Knowledge Base Server를 자동으로 설치하려면:\n\n지엑스피1\n\n### 수동 설치\n\n**필수 조건**\n\n* [Node.js](https://nodejs.org/) (버전 16 이상)\n* [npm](https://www.npmjs.com/) (노드 패키지 관리자)\n\n1. **저장소를 복제합니다.**\n\n ```bash\n git clone \n cd knowledge-base-mcp-server\n ```\n\n2. **종속성 설치:**\n\n ```bash\n npm install\n ```\n\n3. **환경 변수 구성:**\n\n * 서버에서는 `HUGGINGFACE_API_KEY` 환경 변수를 설정해야 합니다. 이 키는 Hugging Face Inference API의 API 키로, 지식 기반 콘텐츠의 임베딩을 생성하는 데 사용됩니다. Hugging Face 웹사이트( )에서 무료 API 키를 받으실 수 있습니다.\n * 서버를 사용하려면 `KNOWLEDGE_BASES_ROOT_DIR` 환경 변수를 설정해야 합니다. 이 변수는 지식 기반 하위 디렉터리가 있는 디렉터리를 지정합니다. 이 변수를 설정하지 않으면 기본값은 `$HOME/knowledge_bases` 입니다. 여기서 `$HOME` 현재 사용자의 홈 디렉터리입니다.\n * 서버는 FAISS 인덱스 경로를 지정하는 `FAISS_INDEX_PATH` 환경 변수를 지원합니다. 설정하지 않으면 기본적으로 `$HOME/knowledge_bases/.faiss` 가 사용됩니다.\n * 서버는 임베딩 생성에 사용할 Hugging Face 모델을 지정하는 `HUGGINGFACE_MODEL_NAME` 환경 변수를 지원합니다. 설정하지 않으면 기본적으로 `sentence-transformers/all-MiniLM-L6-v2` 가 사용됩니다.\n * 이러한 환경 변수는 `.bashrc` 또는 `.zshrc` 파일에서 설정하거나 MCP 설정에서 직접 설정할 수 있습니다.\n\n4. **서버를 빌드하세요:**\n\n ```bash\n npm run build\n ```\n\n5. **MCP 설정에 서버를 추가합니다.**\n\n * `/home/jean/.vscode-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/` 에 있는 `cline_mcp_settings.json` 파일을 편집합니다.\n * `mcpServers` 개체에 다음 구성을 추가합니다.\n\n ```json\n \"knowledge-base-mcp\": {\n \"command\": \"node\",\n \"args\": [\n \"/path/to/knowledge-base-mcp-server/build/index.js\"\n ],\n \"disabled\": false,\n \"autoApprove\": [],\n \"env\": {\n \"KNOWLEDGE_BASES_ROOT_DIR\": \"/path/to/knowledge_bases\",\n \"HUGGINGFACE_API_KEY\": \"YOUR_HUGGINGFACE_API_KEY\",\n },\n \"description\": \"Retrieves similar chunks from the knowledge base based on a query.\"\n },\n ```\n\n * `/path/to/knowledge-base-mcp-server` 서버 디렉토리의 실제 경로로 바꾸세요.\n * `/path/to/knowledge_bases` 지식 기반 디렉토리의 실제 경로로 바꾸세요.\n\n6. **지식 기반 디렉토리를 만듭니다.**\n\n * 각 지식 기반(예: `company` , `it_support` , `onboarding` )에 대해 `KNOWLEDGE_BASES_ROOT_DIR` 내에 하위 디렉토리를 만듭니다.\n * 지식 기반 콘텐츠가 포함된 텍스트 파일(예: `.txt` , `.md` )을 이러한 하위 디렉토리에 넣습니다.\n\n* 서버는 지정된 지식 기반 하위 디렉토리 내의 모든 텍스트 파일(예: `.txt` , `.md` )을 재귀적으로 읽습니다.\n* 서버는 숨겨진 파일과 디렉토리( `.` 으로 시작하는 파일과 디렉토리)를 건너뜁니다.\n* 서버는 각 파일에 대해 SHA256 해시값을 계산하여 숨겨진 `.index` 하위 디렉터리에 있는 동일한 이름의 파일에 저장합니다. 이 해시는 파일이 마지막 인덱싱 이후 수정되었는지 확인하는 데 사용됩니다.\n* 파일 내용은 `langchain/text_splitter` 의 `MarkdownTextSplitter` 를 사용하여 청크로 분할됩니다.\n* 각 청크의 내용은 유사성 검색에 사용되는 FAISS 인덱스에 추가됩니다.\n* FAISS 색인은 서버 시작 시 자동으로 초기화됩니다. 지식 기반 파일의 변경 사항을 확인하고 그에 따라 색인을 업데이트합니다.\n\n## 용법\n\n서버는 두 가지 도구를 제공합니다.\n\n* `list_knowledge_bases` : 사용 가능한 지식 기반을 나열합니다.\n* `retrieve_knowledge` : 쿼리를 기반으로 지식 기반에서 유사한 청크를 검색합니다. 선택적으로, 지식 기반이 지정된 경우 해당 지식 기반만 검색하고, 그렇지 않은 경우 사용 가능한 모든 지식 기반을 고려합니다. 기본적으로 점수가 임계값 2 미만인 문서 청크는 최대 10개까지 반환됩니다. 선택적으로 `threshold` 매개변수를 사용하여 다른 임계값을 지정할 수 있습니다.\n\n이러한 도구는 MCP 인터페이스를 통해 사용할 수 있습니다.\n\n`retrieve_knowledge` 도구는 FAISS 색인을 사용하여 의미론적 검색을 수행합니다. 색인은 서버가 시작되거나 지식 베이스의 파일이 수정될 때 자동으로 업데이트됩니다.\n\n`retrieve_knowledge` 도구의 출력은 다음과 같은 구조를 가진 마크다운 형식의 문자열입니다.\n\n````markdown\n## Semantic Search Results\n\n**Result 1:**\n\n[Content of the most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n---\n\n**Result 2:**\n\n[Content of the second most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n> **Disclaimer:** The provided results might not all be relevant. Please cross-check the relevance of the information.\n````\n\n각 결과에는 가장 유사한 청크의 내용, 소스 파일, 유사도 점수가 포함됩니다.","Korean","ko-KR",{"_29":62,"_33":66,"_38":67},{"_31":63,"_33":64,"_35":65},"提供使用语义搜索功能列出和检索来自不同知识库的内容的工具。","知识库 MCP 服务器","# 知识库 MCP 服务器\n\n[![铁匠徽章](https://smithery.ai/badge/@jeanibarz/knowledge-base-mcp-server)](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server)该 MCP 服务器提供列出和检索来自不同知识库的内容的工具。\n\n## 设置说明\n\n这些说明假设您的系统上安装了 Node.js 和 npm。\n\n### 通过 Smithery 安装\n\n要通过[Smithery](https://smithery.ai/server/@jeanibarz/knowledge-base-mcp-server)自动安装 Claude Desktop 的知识库服务器：\n\n```bash\nnpx -y @smithery/cli install @jeanibarz/knowledge-base-mcp-server --client claude\n```\n\n### 手动安装\n\n**先决条件**\n\n* [Node.js](https://nodejs.org/) （版本 16 或更高版本）\n* [npm](https://www.npmjs.com/) （Node 包管理器）\n\n1. **克隆存储库：**\n\n ```bash\n git clone \n cd knowledge-base-mcp-server\n ```\n\n2. **安装依赖项：**\n\n ```bash\n npm install\n ```\n\n3. **配置环境变量：**\n\n * 服务器需要设置`HUGGINGFACE_API_KEY`环境变量。这是 Hugging Face Inference API 的 API 密钥，用于生成知识库内容的嵌入。您可以从 Hugging Face 网站 ( ) 获取免费的 API 密钥。\n * 服务器需要设置`KNOWLEDGE_BASES_ROOT_DIR`环境变量。此变量指定知识库子目录所在的目录。如果不设置此变量，则默认为`$HOME/knowledge_bases` ，其中`$HOME`是当前用户的主目录。\n * 服务器支持使用`FAISS_INDEX_PATH`环境变量来指定 FAISS 索引的路径。如果未设置，则默认为`$HOME/knowledge_bases/.faiss` 。\n * 服务器支持`HUGGINGFACE_MODEL_NAME`环境变量，用于指定用于生成嵌入的 Hugging Face 模型。如果未设置，则默认为`sentence-transformers/all-MiniLM-L6-v2` 。\n * 您可以在`.bashrc`或`.zshrc`文件中设置这些环境变量，也可以直接在 MCP 设置中设置。\n\n4. **构建服务器：**\n\n ```bash\n npm run build\n ```\n\n5. **将服务器添加到 MCP 设置：**\n\n * 编辑位于`/home/jean/.vscode-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/`的`cline_mcp_settings.json`文件。\n * 向`mcpServers`对象添加以下配置：\n\n ```json\n \"knowledge-base-mcp\": {\n \"command\": \"node\",\n \"args\": [\n \"/path/to/knowledge-base-mcp-server/build/index.js\"\n ],\n \"disabled\": false,\n \"autoApprove\": [],\n \"env\": {\n \"KNOWLEDGE_BASES_ROOT_DIR\": \"/path/to/knowledge_bases\",\n \"HUGGINGFACE_API_KEY\": \"YOUR_HUGGINGFACE_API_KEY\",\n },\n \"description\": \"Retrieves similar chunks from the knowledge base based on a query.\"\n },\n ```\n\n * 将`/path/to/knowledge-base-mcp-server`替换为服务器目录的实际路径。\n * 将`/path/to/knowledge_bases`替换为知识库目录的实际路径。\n\n6. **创建知识库目录：**\n\n * 在`KNOWLEDGE_BASES_ROOT_DIR`中为每个知识库创建子目录（例如`company` 、 `it_support` 、 `onboarding` ）。\n * 将包含知识库内容的文本文件（例如`.txt` 、 `.md` ）放在这些子目录中。\n\n* 服务器递归读取指定知识库子目录内的所有文本文件（例如`.txt` 、 `.md` ）。\n* 服务器跳过隐藏文件和目录（以`.`开头的文件和目录）。\n* 对于每个文件，服务器都会计算 SHA256 哈希值，并将其存储在隐藏的`.index`子目录中的同名文件中。此哈希值用于确定文件自上次索引以来是否被修改过。\n* 使用`langchain/text_splitter`中的`MarkdownTextSplitter`将文件内容分割成块。\n* 然后将每个块的内容添加到 FAISS 索引中，用于相似性搜索。\n* FAISS 索引在服务器启动时自动初始化。它会检查知识库文件中的变化并相应地更新索引。\n\n## 用法\n\n该服务器公开了两个工具：\n\n* `list_knowledge_bases` ：列出可用的知识库。\n* `retrieve_knowledge`知识库 (retrieve\\_knowledge)：根据查询从知识库中检索相似的块。（可选）如果指定了知识库，则仅搜索该知识库；否则，将考虑所有可用的知识库。默认情况下，最多返回 10 个得分低于阈值 2 的文档块。可以使用`threshold`参数设置其他阈值。\n\n您可以通过 MCP 界面使用这些工具。\n\n`retrieve_knowledge`工具使用 FAISS 索引执行语义搜索。当服务器启动或知识库中的文件被修改时，索引会自动更新。\n\n`retrieve_knowledge`工具的输出是 markdown 格式的字符串，具有以下结构：\n\n````markdown\n## Semantic Search Results\n\n**Result 1:**\n\n[Content of the most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n---\n\n**Result 2:**\n\n[Content of the second most similar chunk]\n\n**Source:**\n```json\n{\n \"source\": \"[Path to the file containing the chunk]\"\n}\n```\n\n> **Disclaimer:** The provided results might not all be relevant. Please cross-check the relevance of the information.\n````\n\n每个结果包括最相似的块的内容、源文件和相似度分数。","Chinese","zh-CN","githubUser","locale",{"_71":72,"_73":74,"_13":98,"_107":32,"_108":32,"_109":110,"_113":114,"_137":-5,"_16":138,"_139":140,"_141":142,"_33":34,"_160":127,"_161":-5,"_162":-5,"_163":164,"_165":-5,"_166":167,"_172":-5,"_149":123,"_173":174,"_178":179,"_128":203,"_204":205},"addedAt","2025-03-09T00:06:30.303303310Z","argumentsJsonSchema",{"_75":76,"_95":96,"_82":97},"properties",{"_77":78,"_84":85,"_87":88,"_91":92},"FAISS_INDEX_PATH",{"_79":80,"_31":81,"_82":83},"default","$HOME/knowledge_bases/.faiss","The path to the FAISS index.","type","string","HUGGINGFACE_API_KEY",{"_31":86,"_82":83},"API key for the Hugging Face Inference API, which is used to generate embeddings for the knowledge base content.","HUGGINGFACE_MODEL_NAME",{"_79":89,"_31":90,"_82":83},"sentence-transformers/all-MiniLM-L6-v2","The Hugging Face model to use for generating embeddings.","KNOWLEDGE_BASES_ROOT_DIR",{"_79":93,"_31":94,"_82":83},"$HOME/knowledge_bases","The directory where the knowledge base subdirectories are located.","required",[84],"object",[99,100,101,102,103,104,105,106],"category:knowledge-and-memory","category:search","environment:local","hosting:hybrid","language:typescript","security_grade:a","quality_grade:a","license_grade:a","descriptionMarkdown","descriptionPlainText","githubProject",{"_35":36,"_111":112},"url","https://github.com/jeanibarz/knowledge-base-mcp-server","githubRepository",{"_115":116,"_117":118,"_119":120,"_121":122,"_33":123,"_124":125,"_130":131,"_132":133,"_135":136},"defaultBranch","main","fullName","jeanibarz/knowledge-base-mcp-server","githubTreeId",112192,"language","TypeScript","knowledge-base-mcp-server","owner",{"_126":127,"_128":129},"login","jeanibarz","uid","3jcGcpkM6j","recentStargazers",0,"spdxLicense",{"_33":134},"The Unlicense","stargazers",10,"glamaJson",1464,"inspectable",true,"integrations",[143,153],{"_144":145,"_31":151,"_128":152},"brand",{"_146":147,"_33":148,"_149":150},"logoSvgPath","M12.025 1.13c-5.77 0-10.449 4.647-10.449 10.378 0 1.112.178 2.181.503 3.185.064-.222.203-.444.416-.577a.96.96 0 0 1 .524-.15c.293 0 .584.124.84.284.278.173.48.408.71.694.226.282.458.611.684.951v-.014c.017-.324.106-.622.264-.874s.403-.487.762-.543c.3-.047.596.06.787.203s.31.313.4.467c.15.257.212.468.233.542.01.026.653 1.552 1.657 2.54.616.605 1.01 1.223 1.082 1.912.055.537-.096 1.059-.38 1.572.637.121 1.294.187 1.967.187.657 0 1.298-.063 1.921-.178-.287-.517-.44-1.041-.384-1.581.07-.69.465-1.307 1.081-1.913 1.004-.987 1.647-2.513 1.657-2.539.021-.074.083-.285.233-.542.09-.154.208-.323.4-.467a1.08 1.08 0 0 1 .787-.203c.359.056.604.29.762.543s.247.55.265.874v.015c.225-.34.457-.67.683-.952.23-.286.432-.52.71-.694.257-.16.547-.284.84-.285a.97.97 0 0 1 .524.151c.228.143.373.388.43.625l.006.04a10.3 10.3 0 0 0 .534-3.273c0-5.731-4.678-10.378-10.449-10.378M8.327 6.583a1.5 1.5 0 0 1 .713.174 1.487 1.487 0 0 1 .617 2.013c-.183.343-.762-.214-1.102-.094-.38.134-.532.914-.917.71a1.487 1.487 0 0 1 .69-2.803m7.486 0a1.487 1.487 0 0 1 .689 2.803c-.385.204-.536-.576-.916-.71-.34-.12-.92.437-1.103.094a1.487 1.487 0 0 1 .617-2.013 1.5 1.5 0 0 1 .713-.174m-10.68 1.55a.96.96 0 1 1 0 1.921.96.96 0 0 1 0-1.92m13.838 0a.96.96 0 1 1 0 1.92.96.96 0 0 1 0-1.92M8.489 11.458c.588.01 1.965 1.157 3.572 1.164 1.607-.007 2.984-1.155 3.572-1.164.196-.003.305.12.305.454 0 .886-.424 2.328-1.563 3.202-.22-.756-1.396-1.366-1.63-1.32q-.011.001-.02.006l-.044.026-.01.008-.03.024q-.018.017-.035.036l-.032.04a1 1 0 0 0-.058.09l-.014.025q-.049.088-.11.19a1 1 0 0 1-.083.116 1.2 1.2 0 0 1-.173.18q-.035.029-.075.058a1.3 1.3 0 0 1-.251-.243 1 1 0 0 1-.076-.107c-.124-.193-.177-.363-.337-.444-.034-.016-.104-.008-.2.022q-.094.03-.216.087-.06.028-.125.063l-.13.074q-.067.04-.136.086a3 3 0 0 0-.135.096 3 3 0 0 0-.26.219 2 2 0 0 0-.12.121 2 2 0 0 0-.106.128l-.002.002a2 2 0 0 0-.09.132l-.001.001a1.2 1.2 0 0 0-.105.212q-.013.036-.024.073c-1.139-.875-1.563-2.317-1.563-3.203 0-.334.109-.457.305-.454m.836 10.354c.824-1.19.766-2.082-.365-3.194-1.13-1.112-1.789-2.738-1.789-2.738s-.246-.945-.806-.858-.97 1.499.202 2.362c1.173.864-.233 1.45-.685.64-.45-.812-1.683-2.896-2.322-3.295s-1.089-.175-.938.647 2.822 2.813 2.562 3.244-1.176-.506-1.176-.506-2.866-2.567-3.49-1.898.473 1.23 2.037 2.16c1.564.932 1.686 1.178 1.464 1.53s-3.675-2.511-4-1.297c-.323 1.214 3.524 1.567 3.287 2.405-.238.839-2.71-1.587-3.216-.642-.506.946 3.49 2.056 3.522 2.064 1.29.33 4.568 1.028 5.713-.624m5.349 0c-.824-1.19-.766-2.082.365-3.194 1.13-1.112 1.789-2.738 1.789-2.738s.246-.945.806-.858.97 1.499-.202 2.362c-1.173.864.233 1.45.685.64.451-.812 1.683-2.896 2.322-3.295s1.089-.175.938.647-2.822 2.813-2.562 3.244 1.176-.506 1.176-.506 2.866-2.567 3.49-1.898-.473 1.23-2.037 2.16c-1.564.932-1.686 1.178-1.464 1.53s3.675-2.511 4-1.297c.323 1.214-3.524 1.567-3.287 2.405.238.839 2.71-1.587 3.216-.642.506.946-3.49 2.056-3.522 2.064-1.29.33-4.568 1.028-5.713-.624","Hugging Face","slug","huggingface","Uses the Hugging Face Inference API to generate embeddings for the knowledge base content, with optional model selection through environment variables.","t4i3dw0u2p",{"_144":154,"_31":158,"_128":159},{"_146":155,"_33":156,"_149":157},"M6.0988 5.9175C2.7359 5.9175 0 8.6462 0 12s2.736 6.0825 6.0988 6.0825h11.8024C21.2641 18.0825 24 15.3538 24 12s-2.736-6.0825-6.0988-6.0825ZM5.9774 7.851c.493.0124 1.02.2496 1.273.6228.3673.4592.4778 1.0668.8944 1.4932.5604.6118 1.199 1.1505 1.7161 1.802.4892.5954.8386 1.2937 1.1436 1.9975.1244.2335.1257.5202.31.7197.0908.1204.5346.4483.4383.5645.0555.1204.4702.286.3263.4027-.1944.04-.4129.0476-.5616-.1074-.0549.126-.183.0596-.2819.0432a4 4 0 0 0-.025.0736c-.3288.0219-.5754-.3126-.732-.565-.3111-.168-.6642-.2702-.982-.446-.0182.2895.0452.6485-.231.8353-.014.5565.8436.0656.9222.4804-.061.0067-.1286-.0095-.1774.0373-.2239.2172-.4805-.1645-.7385-.007-.3464.174-.3808.3161-.8096.352-.0237-.0359-.0143-.0592.0059-.0811.1207-.1399.1295-.3046.3356-.3643-.2122-.0334-.3899.0833-.5686.1757-.2323.095-.2304-.2141-.5878.0164-.0396-.0322-.0208-.0615.0018-.0864.0908-.1107.2102-.127.345-.1208-.663-.3686-.9751.4507-1.2813.0432-.092.0243-.1265.1068-.1845.1652-.05-.0548-.0123-.1212-.0099-.1857-.0598-.028-.1356-.041-.1179-.1366-.1171-.0395-.1988.0295-.286.0952-.0787-.0608.0532-.1492.0776-.2125.0702-.1216.23-.025.3111-.1126.2306-.1308.552.0814.8155.0455.203.0255.4544-.1825.3526-.39-.2171-.2767-.179-.6386-.1839-.9695-.0268-.1929-.491-.4382-.6252-.6462-.1659-.1873-.295-.4047-.4243-.6182-.4666-.9008-.3198-2.0584-.9077-2.8947-.266.1466-.6125.0774-.8418-.119-.1238.1125-.1292.2598-.139.4161-.297-.2962-.2593-.8559-.022-1.1855.0969-.1302.2127-.2373.342-.3316.0292-.0213.0391-.0419.0385-.0747.1174-.5267.5764-.7391 1.0694-.7267m12.4071.46c.5575 0 1.0806.2159 1.474.6082s.61.9145.61 1.4704c0 .556-.2167 1.078-.61 1.4698v.0006l-.902.8995a2.08 2.08 0 0 1-.8597.5166l-.0164.0047-.0058.0164a2.05 2.05 0 0 1-.474.7308l-.9018.8995c-.3934.3924-.917.6083-1.4745.6083s-1.0806-.216-1.474-.6083c-.813-.8107-.813-2.1294 0-2.9402l.9019-.8995a2.056 2.056 0 0 1 .858-.5143l.017-.0053.0058-.0158a2.07 2.07 0 0 1 .4752-.7337l.9018-.8995c.3934-.3924.9171-.6083 1.4745-.6083zm0 .8965a1.18 1.18 0 0 0-.8388.3462l-.9018.8995a1.181 1.181 0 0 0-.3427.9252l.0053.0572c.0323.2652.149.5044.3374.6917.13.1296.2733.2114.4471.2686a.9.9 0 0 1 .014.1582.884.884 0 0 1-.2609.6304l-.0554.0554c-.3013-.1028-.5525-.253-.7794-.4792a2.06 2.06 0 0 1-.5761-1.0968l-.0099-.0578-.0461.0368a1.1 1.1 0 0 0-.0876.0794l-.9024.8995c-.4623.461-.4623 1.212 0 1.673.2311.2305.535.346.8394.3461.3043 0 .6077-.1156.8388-.3462l.9019-.8995c.4623-.461.4623-1.2113 0-1.673a1.17 1.17 0 0 0-.4367-.2749 1 1 0 0 1-.014-.1611c0-.2591.1023-.505.2901-.6923.3019.1028.57.2694.7962.495.3007.2999.4994.679.5756 1.0968l.0105.0578.0455-.0373a1.1 1.1 0 0 0 .0887-.0794l.902-.8996c.4622-.461.4628-1.2124 0-1.6735a1.18 1.18 0 0 0-.8395-.3462Zm-9.973 5.1567-.0006.0006c-.0793.3078-.1048.8318-.506.847-.033.1776.1228.2445.2655.1874.141-.0645.2081.0508.2557.1657.2177.0317.5394-.0725.5516-.3298-.325-.1867-.4253-.5418-.5662-.8709","LangChain","langchain","Uses LangChain's MarkdownTextSplitter to split file content into chunks for the knowledge base.","7lfrk7rxyb","namespace","npmPackage","prompts","redditPostForeignId","1j6vg0v","resources","scores",{"_168":169,"_170":169,"_171":169},"license",100,"quality","security","simpleIcon","supportedPlatforms",[175,176,177],"LINUX","WINDOWS","MACOS","tools",[180,187],{"_33":181,"_31":182,"_183":184},"list_knowledge_bases","Lists the available knowledge bases.","inputSchema",{"_82":97,"_95":185,"_75":186},[],{},{"_33":188,"_31":189,"_183":190},"retrieve_knowledge","Retrieves similar chunks from the knowledge base based on a query. Optionally, if a knowledge base is specified, only that one is searched; otherwise, all available knowledge bases are considered. By default, at most 10 documents are returned with a score below a threshold of 2. A different threshold can optionally be provided.",{"_82":97,"_95":191,"_75":193},[192],"query",{"_192":194,"_196":197,"_200":201},{"_82":83,"_31":195},"The query text to use for semantic search.","threshold",{"_82":198,"_31":199},"number","Optional. The maximum similarity score to return.","knowledge_base_name",{"_82":83,"_31":202},"Optional. Name of the knowledge base to query (e.g., 'company', 'it_support', 'onboarding'). If omitted, the search is performed across all available knowledge bases.","n0p6v0o0a4","updatedAt","2025-04-28T11:00:22.433433777Z","meta",[208,211,213,220,223,225,228,231,234,237,240,242,245,248,250,252,254,256,258],{"_209":210},"title","Knowledge Base MCP Server | Glama",{"_212":32,"_33":31},"content",{"_214":215,"_216":217,"_218":219},"href","https://glama.ai/mcp/servers/@jeanibarz/knowledge-base-mcp-server","rel","canonical","tagName","link",{"_212":34,"_221":222},"property","og:title",{"_212":32,"_221":224},"og:description",{"_212":226,"_221":227},"https://glama.ai/logo.png","og:logo",{"_212":229,"_221":230},"https://glama.ai/mcp/servers/@jeanibarz/knowledge-base-mcp-server/og-image","og:image",{"_212":232,"_221":233},"630","og:image:height",{"_212":235,"_221":236},"1200","og:image:width",{"_212":238,"_221":239},"website","og:type",{"_212":215,"_221":241},"og:url",{"_212":243,"_221":244},"Glama – MCP Hosting Platform","og:site_name",{"_212":246,"_33":247},"summary_large_image","twitter:card",{"_212":243,"_33":249},"twitter:title",{"_212":32,"_33":251},"twitter:description",{"_212":229,"_33":253},"twitter:image",{"_212":232,"_33":255},"twitter:image:height",{"_212":235,"_33":257},"twitter:image:width",{"_259":260},"script:ld+json",{"_261":262,"_263":264},"@context","http://schema.org","@graph",[265],{"_261":266,"_267":268,"_269":270},"https://schema.org","@type","BreadcrumbList","itemListElement",[271,278,282,286],{"_267":272,"_273":274,"_33":275,"_276":277},"ListItem","item","https://glama.ai/","Glama","position",1,{"_267":272,"_273":279,"_33":280,"_276":281},"https://glama.ai/mcp","MCP",2,{"_267":272,"_273":283,"_33":284,"_276":285},"https://glama.ai/mcp/servers","Servers",3,{"_267":272,"_273":215,"_33":34,"_276":287},4,"visitorIsMaintainer",false,"routes/_public/mcp/servers/~namespace/~slug/_pages/reviews/_route",{"_68":-5,"_29":292,"_206":324,"_359":-5},{"_71":72,"_73":293,"_13":300,"_107":32,"_108":32,"_109":301,"_113":302,"_137":-5,"_16":138,"_139":140,"_141":305,"_33":34,"_160":127,"_161":-5,"_162":-5,"_163":164,"_165":-5,"_166":310,"_172":-5,"_149":123,"_173":311,"_178":312,"_128":203,"_204":205},{"_75":294,"_95":299,"_82":97},{"_77":295,"_84":296,"_87":297,"_91":298},{"_79":80,"_31":81,"_82":83},{"_31":86,"_82":83},{"_79":89,"_31":90,"_82":83},{"_79":93,"_31":94,"_82":83},[84],[99,100,101,102,103,104,105,106],{"_35":36,"_111":112},{"_115":116,"_117":118,"_119":120,"_121":122,"_33":123,"_124":303,"_130":131,"_132":304,"_135":136},{"_126":127,"_128":129},{"_33":134},[306,308],{"_144":307,"_31":151,"_128":152},{"_146":147,"_33":148,"_149":150},{"_144":309,"_31":158,"_128":159},{"_146":155,"_33":156,"_149":157},{"_168":169,"_170":169,"_171":169},[175,176,177],[313,317],{"_33":181,"_31":182,"_183":314},{"_82":97,"_95":315,"_75":316},[],{},{"_33":188,"_31":189,"_183":318},{"_82":97,"_95":319,"_75":320},[192],{"_192":321,"_196":322,"_200":323},{"_82":83,"_31":195},{"_82":198,"_31":199},{"_82":83,"_31":202},[325,327,329,331,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347],{"_209":326},"Reviews | Knowledge Base MCP Server | Glama",{"_212":328,"_33":31},"Read community reviews and feedback for Knowledge Base MCP Server. Discover user experiences, ratings, strengths, and potential limitations of this MCP implementation from actual users.",{"_214":330,"_216":217,"_218":219},"https://glama.ai/mcp/servers/@jeanibarz/knowledge-base-mcp-server/reviews",{"_212":332,"_221":222},"Reviews | Knowledge Base MCP Server",{"_212":328,"_221":224},{"_212":226,"_221":227},{"_212":229,"_221":230},{"_212":232,"_221":233},{"_212":235,"_221":236},{"_212":238,"_221":239},{"_212":330,"_221":241},{"_212":243,"_221":244},{"_212":246,"_33":247},{"_212":243,"_33":249},{"_212":328,"_33":251},{"_212":229,"_33":253},{"_212":232,"_33":255},{"_212":235,"_33":257},{"_259":348},{"_261":262,"_263":349},[350],{"_261":266,"_267":268,"_269":351},[352,353,354,355,356],{"_267":272,"_273":274,"_33":275,"_276":277},{"_267":272,"_273":279,"_33":280,"_276":281},{"_267":272,"_273":283,"_33":284,"_276":285},{"_267":272,"_273":215,"_33":34,"_276":287},{"_267":272,"_273":330,"_33":357,"_276":358},"Reviews",5,"review","actionData","errors"]

Rate & Review

MCP directory API