[{"_1":2,"_394":-5,"_395":-5},"loaderData",{"_3":4,"_22":23,"_24":25,"_314":315},"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",7058884,"membership","referenceId","01971406-43e3-74e8-b880-2f000258007a","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_68":-5,"_69":28,"_29":70,"_231":232,"_312":313},"availableLocales",[28,40,47,54,61],{"_29":30,"_33":37,"_38":39},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","The Atla MCP Server provides a standardized interface for LLMs to interact with the Atla API for state-of-the-art LLMJ evaluation.","name","Atla","readme","# Atla MCP Server\n\nAn MCP server implementation providing a standardized interface for LLMs to interact with the Atla API for state-of-the-art LLMJ evaluation.\n\n> Learn more about Atla [here](https://docs.atla-ai.com). Learn more about the Model Context Protocol [here](https://modelcontextprotocol.io).\n\n\n $\"Atla$ \n\n\n## Available Tools\n\n- `evaluate_llm_response`: Evaluate an LLM's response to a prompt using a given evaluation criteria. This function uses an Atla evaluation model under the hood to return a dictionary containing a score for the model's response and a textual critique containing feedback on the model's response.\n- `evaluate_llm_response_on_multiple_criteria`: Evaluate an LLM's response to a prompt across _multiple_ evaluation criteria. This function uses an Atla evaluation model under the hood to return a list of dictionaries, each containing an evaluation score and critique for a given criteria.\n\n## Usage\n\n> To use the MCP server, you will need an Atla API key. You can find your existing API key [here](https://www.atla-ai.com/sign-in) or create a new one [here](https://www.atla-ai.com/sign-up).\n\n### Installation\n\n> We recommend using `uv` to manage the Python environment. See [here](https://docs.astral.sh/uv/getting-started/installation/) for installation instructions.\n\n### Manually running the server\n\nOnce you have `uv` installed and have your Atla API key, you can manually run the MCP server using `uvx` (which is provided by `uv`):\n\n```bash\nATLA_API_KEY= uvx atla-mcp-server\n```\n\n### Connecting to the server\n\n> Having issues or need help connecting to another client? Feel free to open an issue or [contact us](mailto:support@atla-ai.com)!\n\n#### OpenAI Agents SDK\n\n> For more details on using the OpenAI Agents SDK with MCP servers, refer to the [official documentation](https://openai.github.io/openai-agents-python/).\n\n1. Install the OpenAI Agents SDK:\n\n```shell\npip install openai-agents\n```\n\n2. Use the OpenAI Agents SDK to connect to the server:\n\n```python\nimport os\n\nfrom agents import Agent\nfrom agents.mcp import MCPServerStdio\n\nasync with MCPServerStdio(\n params={\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\"ATLA_API_KEY\": os.environ.get(\"ATLA_API_KEY\")}\n }\n ) as atla_mcp_server:\n ...\n```\n\n#### Claude Desktop\n\n> For more details on configuring MCP servers in Claude Desktop, refer to the [official MCP quickstart guide](https://modelcontextprotocol.io/quickstart/user).\n\n1. Add the following to your `claude_desktop_config.json` file:\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n2. **Restart Claude Desktop** to apply the changes.\n\nYou should now see options from `atla-mcp-server` in the list of available MCP tools.\n\n#### Cursor\n\n> For more details on configuring MCP servers in Cursor, refer to the [official documentation](https://docs.cursor.com/context/model-context-protocol).\n\n1. Add the following to your `.cursor/mcp.json` file:\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\nYou should now see `atla-mcp-server` in the list of available MCP servers.\n\n## Contributing\n\nContributions are welcome! Please see the [CONTRIBUTING.md](CONTRIBUTING.md) file for details.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n","English","tag","en-US",{"_29":41,"_33":45,"_38":46},{"_31":42,"_33":43,"_35":44},"El servidor Atla MCP proporciona una interfaz estandarizada para que los LLM interactúen con la API de Atla para una evaluación LLMJ de última generación.","Atlas","# Servidor MCP de Atla\n\nUna implementación de servidor MCP que proporciona una interfaz estandarizada para que los LLM interactúen con la API de Atla para la evaluación de LLMJ de última generación.\n\n> Obtenga más información sobre Atla [aquí](https://docs.atla-ai.com) . Obtenga más información sobre el Protocolo de Contexto de Modelo [aquí](https://modelcontextprotocol.io) .\n\n## Herramientas disponibles\n\n* `evaluate_llm_response` : Evalúa la respuesta de un LLM a una solicitud utilizando criterios de evaluación determinados. Esta función utiliza un modelo de evaluación Atla para generar un diccionario con una puntuación de la respuesta del modelo y una crítica textual con comentarios sobre la misma.\n* `evaluate_llm_response_on_multiple_criteria` : Evalúa la respuesta de un LLM a una solicitud según *múltiples* criterios de evaluación. Esta función utiliza un modelo de evaluación de Atla para generar una lista de diccionarios, cada uno con una puntuación de evaluación y una crítica para un criterio determinado.\n\n## Uso\n\n> Para usar el servidor MCP, necesitará una clave API de Atla. Puede encontrar su clave API actual [aquí](https://www.atla-ai.com/sign-in) o crear una nueva [aquí](https://www.atla-ai.com/sign-up) .\n\n### Instalación\n\n> Recomendamos usar `uv` para administrar el entorno de Python. Consulte [aquí](https://docs.astral.sh/uv/getting-started/installation/) las instrucciones de instalación.\n\n### Ejecución manual del servidor\n\nUna vez que haya instalado `uv` y tenga su clave API de Atla, puede ejecutar manualmente el servidor MCP usando `uvx` (que es proporcionado por `uv` ):\n\n```bash\nATLA_API_KEY= uvx atla-mcp-server\n```\n\n### Conectando al servidor\n\n> ¿Tienes problemas o necesitas ayuda para conectarte con otro cliente? ¡No dudes en abrir una incidencia o [contactarnos](mailto:support@atla-ai.com) !\n\n#### SDK de agentes de OpenAI\n\n> Para obtener más detalles sobre el uso del SDK de OpenAI Agents con servidores MCP, consulte la [documentación oficial](https://openai.github.io/openai-agents-python/) .\n\n1. Instalar el SDK de OpenAI Agents:\n\n```shell\npip install openai-agents\n```\n\n2. Utilice el SDK de OpenAI Agents para conectarse al servidor:\n\n```python\nimport os\n\nfrom agents import Agent\nfrom agents.mcp import MCPServerStdio\n\nasync with MCPServerStdio(\n params={\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\"ATLA_API_KEY\": os.environ.get(\"ATLA_API_KEY\")}\n }\n ) as atla_mcp_server:\n ...\n```\n\n#### Escritorio de Claude\n\n> Para obtener más detalles sobre la configuración de servidores MCP en Claude Desktop, consulte la [guía de inicio rápido oficial de MCP](https://modelcontextprotocol.io/quickstart/user) .\n\n1. Agregue lo siguiente a su archivo `claude_desktop_config.json` :\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n2. **Reinicie Claude Desktop** para aplicar los cambios.\n\nAhora debería ver las opciones de `atla-mcp-server` en la lista de herramientas MCP disponibles.\n\n#### Cursor\n\n> Para obtener más detalles sobre la configuración de servidores MCP en Cursor, consulte la [documentación oficial](https://docs.cursor.com/context/model-context-protocol) .\n\n1. Agregue lo siguiente a su archivo `.cursor/mcp.json` :\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\nAhora debería ver `atla-mcp-server` en la lista de servidores MCP disponibles.\n\n## Contribuyendo\n\n¡Agradecemos sus contribuciones! Consulte el archivo [CONTRIBUTING.md](CONTRIBUTING.md) para más detalles.\n\n## Licencia\n\nEste proyecto está licenciado bajo la Licencia MIT. Consulte el archivo [de LICENCIA](LICENSE) para más detalles.","Spanish","es-ES",{"_29":48,"_33":52,"_38":53},{"_31":49,"_33":50,"_35":51},"Atla MCP サーバーは、最先端の LLMJ 評価のために LLM が Atla API と対話するための標準化されたインターフェースを提供します。","アトラ","# Atla MCP サーバー\n\n最先端の LLMJ 評価のために LLM が Atla API と対話するための標準化されたインターフェースを提供する MCP サーバー実装。\n\n> Atlaの詳細については、[こちらを](https://docs.atla-ai.com)ご覧ください。Model Context Protocolの詳細については、[こちらを](https://modelcontextprotocol.io)ご覧ください。\n\n## 利用可能なツール\n\n* `evaluate_llm_response` : 指定された評価基準を用いて、LLMのプロンプトへの応答を評価します。この関数は、内部的にAtla評価モデルを使用して、モデルの応答のスコアを含む辞書と、モデルの応答に対するフィードバックを含むテキスト形式の批評を返します。\n* `evaluate_llm_response_on_multiple_criteria` : プロンプトに対するLLMの応答を*複数の*評価基準に基づいて評価します。この関数は、内部的にAtla評価モデルを使用して、指定された基準に対する評価スコアと批評を含む辞書のリストを返します。\n\n## 使用法\n\n> MCPサーバーを使用するには、Atla APIキーが必要です。既存のAPIキーは[こちらで](https://www.atla-ai.com/sign-in)確認するか、[こちらから](https://www.atla-ai.com/sign-up)新しいAPIキーを作成してください。\n\n### インストール\n\n> Python環境の管理には`uv`使用をお勧めします。インストール手順については[こちらを](https://docs.astral.sh/uv/getting-started/installation/)ご覧ください。\n\n### サーバーを手動で実行する\n\n`uv`をインストールして Atla API キーを取得したら、 `uvx` ( `uv`によって提供される) を使用して MCP サーバーを手動で実行できます。\n\n```bash\nATLA_API_KEY= uvx atla-mcp-server\n```\n\n### サーバーに接続しています\n\n> 別のクライアントへの接続で問題が発生したり、サポートが必要な場合は、お気軽に問題を報告したり、[お問い合わせください](mailto:support@atla-ai.com)。\n\n#### OpenAIエージェントSDK\n\n> OpenAI Agents SDK を MCP サーバーで使用する方法の詳細については、[公式ドキュメント](https://openai.github.io/openai-agents-python/)を参照してください。\n\n1. OpenAI Agents SDK をインストールします。\n\n```shell\npip install openai-agents\n```\n\n2. OpenAI Agents SDK を使用してサーバーに接続します。\n\n```python\nimport os\n\nfrom agents import Agent\nfrom agents.mcp import MCPServerStdio\n\nasync with MCPServerStdio(\n params={\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\"ATLA_API_KEY\": os.environ.get(\"ATLA_API_KEY\")}\n }\n ) as atla_mcp_server:\n ...\n```\n\n#### クロードデスクトップ\n\n> Claude Desktop で MCP サーバーを構成する方法の詳細については、[公式の MCP クイックスタートガイド](https://modelcontextprotocol.io/quickstart/user)を参照してください。\n\n1. `claude_desktop_config.json`ファイルに次の内容を追加します。\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n2. 変更を適用するには、 **Claude Desktop を再起動します**。\n\n利用可能な MCP ツールのリストに、 `atla-mcp-server`のオプションが表示されるようになります。\n\n#### カーソル\n\n> Cursor で MCP サーバーを構成する方法の詳細については、[公式ドキュメント](https://docs.cursor.com/context/model-context-protocol)を参照してください。\n\n1. `.cursor/mcp.json`ファイルに次のコードを追加します。\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n利用可能な MCP サーバーのリストに`atla-mcp-server`表示されるはずです。\n\n## 貢献\n\n貢献を歓迎します！詳細については、 [CONTRIBUTING.md](CONTRIBUTING.md)ファイルをご覧ください。\n\n## ライセンス\n\nこのプロジェクトはMITライセンスの下で提供されています。詳細は[LICENSE](LICENSE)ファイルをご覧ください。","Japanese","ja-JP",{"_29":55,"_33":59,"_38":60},{"_31":56,"_33":57,"_35":58},"Atla MCP 서버는 LLM이 Atla API와 상호 작용하여 최첨단 LLMJ 평가를 수행할 수 있는 표준화된 인터페이스를 제공합니다.","아틀라","# Atla MCP 서버\n\nLLM이 Atla API와 상호 작용하여 최첨단 LLMJ 평가를 수행할 수 있는 표준화된 인터페이스를 제공하는 MCP 서버 구현입니다.\n\n> Atla에 대한 자세한 내용은 [여기를](https://docs.atla-ai.com) 참조하세요. 모델 컨텍스트 프로토콜에 대한 자세한 내용은 [여기를](https://modelcontextprotocol.io) 참조하세요.\n\n## 사용 가능한 도구\n\n* `evaluate_llm_response` : 주어진 평가 기준을 사용하여 프롬프트에 대한 LLM의 응답을 평가합니다. 이 함수는 Atla 평가 모델을 기반으로 모델의 응답 점수와 모델 응답에 대한 피드백을 포함하는 텍스트 비평을 포함하는 사전을 반환합니다.\n* `evaluate_llm_response_on_multiple_criteria` : LLM의 프롬프트 응답을 *여러* 평가 기준에 따라 평가합니다. 이 함수는 Atla 평가 모델을 기반으로 각 기준에 대한 평가 점수와 비평을 포함하는 사전 목록을 반환합니다.\n\n## 용법\n\n> MCP 서버를 사용하려면 Atla API 키가 필요합니다. 기존 API 키는 [여기](https://www.atla-ai.com/sign-up) [에서](https://www.atla-ai.com/sign-in) 확인하거나 새 API 키를 생성하세요.\n\n### 설치\n\n> Python 환경을 관리하려면 `uv` 사용하는 것이 좋습니다. 설치 지침은 [여기를](https://docs.astral.sh/uv/getting-started/installation/) 참조하세요.\n\n### 수동으로 서버 실행\n\n`uv` 설치하고 Atla API 키를 받으면 `uvx` ( `uv` 에서 제공)를 사용하여 MCP 서버를 수동으로 실행할 수 있습니다.\n\n지엑스피1\n\n### 서버에 연결\n\n> 문제가 있거나 다른 클라이언트 연결에 도움이 필요하신가요? 언제든지 문제를 제기하시거나 [저희에게 문의해 주세요](mailto:support@atla-ai.com) !\n\n#### OpenAI 에이전트 SDK\n\n> MCP 서버와 함께 OpenAI Agents SDK를 사용하는 방법에 대한 자세한 내용은 [공식 문서를](https://openai.github.io/openai-agents-python/) 참조하세요.\n\n1. OpenAI 에이전트 SDK를 설치하세요:\n\n```shell\npip install openai-agents\n```\n\n2. OpenAI Agents SDK를 사용하여 서버에 연결합니다.\n\n```python\nimport os\n\nfrom agents import Agent\nfrom agents.mcp import MCPServerStdio\n\nasync with MCPServerStdio(\n params={\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\"ATLA_API_KEY\": os.environ.get(\"ATLA_API_KEY\")}\n }\n ) as atla_mcp_server:\n ...\n```\n\n#### 클로드 데스크탑\n\n> Claude Desktop에서 MCP 서버를 구성하는 방법에 대한 자세한 내용은 [공식 MCP 빠른 시작 가이드를](https://modelcontextprotocol.io/quickstart/user) 참조하세요.\n\n1. `claude_desktop_config.json` 파일에 다음을 추가하세요.\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n2. 변경 사항을 적용하려면 **Claude Desktop을 다시 시작하세요** .\n\n이제 사용 가능한 MCP 도구 목록에서 `atla-mcp-server` 의 옵션을 볼 수 있습니다.\n\n#### 커서\n\n> Cursor에서 MCP 서버를 구성하는 방법에 대한 자세한 내용은 [공식 문서를](https://docs.cursor.com/context/model-context-protocol) 참조하세요.\n\n1. `.cursor/mcp.json` 파일에 다음을 추가하세요.\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n이제 사용 가능한 MCP 서버 목록에 `atla-mcp-server` 표시됩니다.\n\n## 기여하다\n\n기여를 환영합니다! 자세한 내용은 [CONTRIBUTING.md](CONTRIBUTING.md) 파일을 참조하세요.\n\n## 특허\n\n이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 [라이선스](LICENSE) 파일을 참조하세요.","Korean","ko-KR",{"_29":62,"_33":66,"_38":67},{"_31":63,"_33":64,"_35":65},"Atla MCP 服务器为 LLM 提供了标准化接口，以便与 Atla API 进行交互，实现最先进的 LLMJ 评估。","阿特拉","# Atla MCP 服务器\n\nMCP 服务器实现为 LLM 提供标准化接口，以便与 Atla API 交互，实现最先进的 LLMJ 评估。\n\n> 点击[此处](https://docs.atla-ai.com)了解更多关于 Atla 的信息。点击[此处](https://modelcontextprotocol.io)了解更多关于模型上下文协议的信息。\n\n## 可用工具\n\n* `evaluate_llm_response` ：使用给定的评估标准评估法学硕士 (LLM) 对提示的响应。此函数使用 Atla 评估模型，返回一个字典，其中包含模型响应的分数以及包含对模型响应反馈的文本评论。\n* 评估 LLM 的答案`evaluate_llm_response_on_multiple_criteria`符合*多个*评估标准。此函数使用 Atla 评估模型，返回一个字典列表，每个字典包含针对给定标准的评估分数和评论。\n\n## 用法\n\n> 要使用 MCP 服务器，您需要一个 Atla API 密钥。您可以[在这里](https://www.atla-ai.com/sign-in)找到您现有的 API 密钥，或[在这里](https://www.atla-ai.com/sign-up)创建一个新的。\n\n### 安装\n\n> 我们建议使用`uv`来管理 Python 环境。安装说明请参阅[此处](https://docs.astral.sh/uv/getting-started/installation/)。\n\n### 手动运行服务器\n\n安装`uv`并拥有 Atla API 密钥后，您可以使用`uvx` （由`uv`提供）手动运行 MCP 服务器：\n\n```bash\nATLA_API_KEY= uvx atla-mcp-server\n```\n\n### 连接到服务器\n\n> 遇到问题或需要帮助连接到其他客户端？欢迎随时提交问题或[联系我们](mailto:support@atla-ai.com)！\n\n#### OpenAI代理SDK\n\n> 有关将 OpenAI Agents SDK 与 MCP 服务器一起使用的更多详细信息，请参阅[官方文档](https://openai.github.io/openai-agents-python/)。\n\n1. 安装 OpenAI Agents SDK：\n\n```shell\npip install openai-agents\n```\n\n2. 使用 OpenAI Agents SDK 连接到服务器：\n\n```python\nimport os\n\nfrom agents import Agent\nfrom agents.mcp import MCPServerStdio\n\nasync with MCPServerStdio(\n params={\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\"ATLA_API_KEY\": os.environ.get(\"ATLA_API_KEY\")}\n }\n ) as atla_mcp_server:\n ...\n```\n\n#### 克劳德桌面\n\n> 有关在 Claude Desktop 中配置 MCP 服务器的更多详细信息，请参阅[官方 MCP 快速入门指南](https://modelcontextprotocol.io/quickstart/user)。\n\n1. 将以下内容添加到您的`claude_desktop_config.json`文件中：\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n2. **重新启动 Claude Desktop**以应用更改。\n\n您现在应该在可用的 MCP 工具列表中看到来自`atla-mcp-server`的选项。\n\n#### 光标\n\n> 有关在 Cursor 中配置 MCP 服务器的更多详细信息，请参阅[官方文档](https://docs.cursor.com/context/model-context-protocol)。\n\n1. 将以下内容添加到您的`.cursor/mcp.json`文件：\n\n```json\n{\n \"mcpServers\": {\n \"atla-mcp-server\": {\n \"command\": \"uvx\",\n \"args\": [\"atla-mcp-server\"],\n \"env\": {\n \"ATLA_API_KEY\": \"\"\n }\n }\n }\n}\n```\n\n您现在应该在可用的 MCP 服务器列表中看到`atla-mcp-server` 。\n\n## 贡献\n\n欢迎贡献！详情请参阅[CONTRIBUTING.md](CONTRIBUTING.md)文件。\n\n## 执照\n\n本项目遵循 MIT 许可证。详情请参阅[LICENSE](LICENSE)文件。","Chinese","zh-CN","githubUser","locale",{"_71":72,"_73":74,"_13":85,"_96":32,"_97":32,"_98":99,"_102":103,"_125":-5,"_16":126,"_127":128,"_129":130,"_33":34,"_141":116,"_142":-5,"_143":-5,"_144":-5,"_145":-5,"_146":147,"_152":-5,"_137":112,"_153":154,"_158":159,"_117":228,"_229":230},"addedAt","2025-04-17T09:55:50.536536720Z","argumentsJsonSchema",{"_75":76,"_82":83,"_80":84},"properties",{"_77":78},"ATLA_API_KEY",{"_31":79,"_80":81},"Your Atla API key, required to interact with the Atla API for LLM 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0 0-.5157-4.9108 6.0462 6.0462 0 0 0-6.5098-2.9A6.0651 6.0651 0 0 0 4.9807 4.1818a5.9847 5.9847 0 0 0-3.9977 2.9 6.0462 6.0462 0 0 0 .7427 7.0966 5.98 5.98 0 0 0 .511 4.9107 6.051 6.051 0 0 0 6.5146 2.9001A5.9847 5.9847 0 0 0 13.2599 24a6.0557 6.0557 0 0 0 5.7718-4.2058 5.9894 5.9894 0 0 0 3.9977-2.9001 6.0557 6.0557 0 0 0-.7475-7.0729zm-9.022 12.6081a4.4755 4.4755 0 0 1-2.8764-1.0408l.1419-.0804 4.7783-2.7582a.7948.7948 0 0 0 .3927-.6813v-6.7369l2.02 1.1686a.071.071 0 0 1 .038.052v5.5826a4.504 4.504 0 0 1-4.4945 4.4944zm-9.6607-4.1254a4.4708 4.4708 0 0 1-.5346-3.0137l.142.0852 4.783 2.7582a.7712.7712 0 0 0 .7806 0l5.8428-3.3685v2.3324a.0804.0804 0 0 1-.0332.0615L9.74 19.9502a4.4992 4.4992 0 0 1-6.1408-1.6464zM2.3408 7.8956a4.485 4.485 0 0 1 2.3655-1.9728V11.6a.7664.7664 0 0 0 .3879.6765l5.8144 3.3543-2.0201 1.1685a.0757.0757 0 0 1-.071 0l-4.8303-2.7865A4.504 4.504 0 0 1 2.3408 7.872zm16.5963 3.8558L13.1038 8.364 15.1192 7.2a.0757.0757 0 0 1 .071 0l4.8303 2.7913a4.4944 4.4944 0 0 1-.6765 8.1042v-5.6772a.79.79 0 0 0-.407-.667zm2.0107-3.0231l-.142-.0852-4.7735-2.7818a.7759.7759 0 0 0-.7854 0L9.409 9.2297V6.8974a.0662.0662 0 0 1 .0284-.0615l4.8303-2.7866a4.4992 4.4992 0 0 1 6.6802 4.66zM8.3065 12.863l-2.02-1.1638a.0804.0804 0 0 1-.038-.0567V6.0742a4.4992 4.4992 0 0 1 7.3757-3.4537l-.142.0805L8.704 5.459a.7948.7948 0 0 0-.3927.6813zm1.0976-2.3654l2.602-1.4998 2.6069 1.4998v2.9994l-2.5974 1.4997-2.6067-1.4997Z","OpenAI","slug","openai","Provides compatibility with the OpenAI Agents SDK, allowing users to connect to the Atla MCP server for LLM evaluation services.","02sq5ot260","namespace","npmPackage","prompts","redditPostForeignId","resources","scores",{"_148":149,"_150":149,"_151":149},"license",100,"quality","security","simpleIcon","supportedPlatforms",[155,156,157],"WINDOWS","MACOS","LINUX","tools",[160,207],{"_33":161,"_31":162,"_163":164},"evaluate_llm_response","Evaluate an LLM's response to a prompt using a given evaluation criteria.\n\n This function uses an Atla evaluation model under the hood to return a dictionary\n containing a score for the model's response and a textual critique containing\n feedback on the model's response.\n\n Returns:\n dict[str, str]: A dictionary containing the evaluation score and critique, in\n the format `{\"score\": , \"critique\": }`.\n ","inputSchema",{"_80":84,"_165":166,"_82":167,"_75":171},"title","evaluate_llm_responseArguments",[168,169,170],"evaluation_criteria","llm_prompt","llm_response",{"_172":173,"_169":178,"_186":187,"_170":190,"_168":197,"_203":204},"model_id",{"_165":174,"_175":176,"_31":177},"Model Id","default","atla-selene","The Atla model ID to use for evaluation. `atla-selene` is the flagship Atla model, optimized for the highest all-round performance. `atla-selene-mini` is a compact model that is generally faster and cheaper to run. Defaults to `atla-selene`.",{"_165":179,"_180":181,"_31":185},"Llm Prompt","examples",[182,183,184],"What is the capital of the moon?","Explain the difference between supervised and unsupervised learning.","Can you summarize the main idea behind transformers in NLP?","The prompt given to an LLM to generate the `llm_response` to be evaluated.","llm_context",{"_165":188,"_175":-5,"_31":189},"Llm Context","Additional context or information provided to the model during generation. This is useful in cases where the model was provided with additional information that is not part of the `llm_prompt` or `expected_llm_output` (e.g., a RAG retrieval context). Defaults to None.",{"_165":191,"_180":192,"_31":196},"Llm Response",[193,194,195],"The Moon doesn't have a capital — it has no countries, governments, or permanent residents","Supervised learning uses labeled data to train models to make predictions or classifications. Unsupervised learning, on the other hand, works with unlabeled data to uncover hidden patterns or groupings, such as through clustering or dimensionality reduction.","Transformers are neural network architectures designed for sequence modeling tasks like NLP. They rely on self-attention mechanisms to weigh the importance of different input tokens, enabling parallel processing of input data. Unlike RNNs, they don't process sequentially, which allows for faster training and better handling of long-range dependencies.","The output generated by the model in response to the `llm_prompt`, which needs to be evaluated.",{"_165":198,"_180":199,"_31":202},"Evaluation Criteria",[200,201],"Evaluate how well the response fulfills the requirements of the instruction by providing relevant information. This includes responding in accordance with the explicit and implicit purpose of given instruction.\n\n Score 1: The response is completely unrelated to the instruction, or the model entirely misunderstands the instruction.\n Score 2: Most of the key points in the response are irrelevant to the instruction, and the response misses major requirements of the instruction.\n Score 3: Some major points in the response contain irrelevant information or miss some requirements of the instruction.\n Score 4: The response is relevant to the instruction but misses minor requirements of the instruction.\n Score 5: The response is perfectly relevant to the instruction, and the model fulfills all of the requirements of the instruction.\n\n Your score should be an integer between 1 and 5.","Evaluate whether the information provided in the response is correct given the reference response.\n Ignore differences in punctuation and phrasing between the response and reference response.\n It is okay if the response contains more information than the reference response, as long as it does not contain any conflicting statements.\n\n Binary scoring\n \"No\": The response is not factually accurate when compared against the reference response or includes conflicting statements.\n \"Yes\": The response is supported by the reference response and does not contain conflicting statements.\n\n Your score should be either \"No\" or \"Yes\".\n","The specific criteria or instructions on which to evaluate the model output. A good evaluation criteria should provide the model with: (1) a description of the evaluation task, (2) a rubric of possible scores and their corresponding criteria, and (3) a final sentence clarifying expected score format. A good evaluation criteria should also be specific and focus on a single aspect of the model output. To evaluate a model's response on multiple criteria, use the `evaluate_llm_response_on_multiple_criteria` function and create individual criteria for each relevant evaluation task. Typical rubrics score responses either on a Likert scale from 1 to 5 or binary scale with scores of 'Yes' or 'No', depending on the specific evaluation task.","expected_llm_output",{"_165":205,"_175":-5,"_31":206},"Expected Llm Output","A reference or ideal answer to compare against the `llm_response`. This is useful in cases where a specific output is expected from the model. Defaults to None.",{"_33":208,"_31":209,"_163":210},"evaluate_llm_response_on_multiple_criteria","Evaluate an LLM's response to a prompt across *multiple* evaluation criteria.\n\n This function uses an Atla evaluation model under the hood to return a list of\n dictionaries, each containing an evaluation score and critique for a given\n criteria.\n\n Returns:\n list[dict[str, str]]: A list of dictionaries containing the evaluation score\n and critique, in the format `{\"score\": , \"critique\": }`.\n The order of the dictionaries in the list will match the order of the\n criteria in the `evaluation_criteria_list` argument.\n ",{"_80":84,"_165":211,"_82":212,"_75":214},"evaluate_llm_response_on_multiple_criteriaArguments",[213,169,170],"evaluation_criteria_list",{"_172":215,"_169":216,"_186":218,"_170":219,"_203":221,"_213":222},{"_165":174,"_175":176,"_31":177},{"_165":179,"_180":217,"_31":185},[182,183,184],{"_165":188,"_175":-5,"_31":189},{"_165":191,"_180":220,"_31":196},[193,194,195],{"_165":205,"_175":-5,"_31":206},{"_80":223,"_224":225,"_165":227},"array","items",{"_180":226,"_31":202},[200,201],"Evaluation Criteria List","0gm3vxdtdx","updatedAt","2025-04-22T22:00:22.057057888Z","meta",[233,235,237,244,247,249,252,255,258,261,264,266,269,272,274,276,278,280,282],{"_165":234},"Atla | Glama",{"_236":32,"_33":31},"content",{"_238":239,"_240":241,"_242":243},"href","https://glama.ai/mcp/servers/@atla-ai/atla-mcp-server","rel","canonical","tagName","link",{"_236":34,"_245":246},"property","og:title",{"_236":32,"_245":248},"og:description",{"_236":250,"_245":251},"https://glama.ai/logo.png","og:logo",{"_236":253,"_245":254},"https://glama.ai/mcp/servers/@atla-ai/atla-mcp-server/og-image","og:image",{"_236":256,"_245":257},"630","og:image:height",{"_236":259,"_245":260},"1200","og:image:width",{"_236":262,"_245":263},"website","og:type",{"_236":239,"_245":265},"og:url",{"_236":267,"_245":268},"Glama – MCP Hosting Platform","og:site_name",{"_236":270,"_33":271},"summary_large_image","twitter:card",{"_236":267,"_33":273},"twitter:title",{"_236":32,"_33":275},"twitter:description",{"_236":253,"_33":277},"twitter:image",{"_236":256,"_33":279},"twitter:image:height",{"_236":259,"_33":281},"twitter:image:width",{"_283":284},"script:ld+json",{"_285":286,"_287":288},"@context","http://schema.org","@graph",[289],{"_285":290,"_291":292,"_293":294},"https://schema.org","@type","BreadcrumbList","itemListElement",[295,302,306,310],{"_291":296,"_297":298,"_33":299,"_300":301},"ListItem","item","https://glama.ai/","Glama","position",1,{"_291":296,"_297":303,"_33":304,"_300":305},"https://glama.ai/mcp","MCP",2,{"_291":296,"_297":307,"_33":308,"_300":309},"https://glama.ai/mcp/servers","Servers",3,{"_291":296,"_297":239,"_33":34,"_300":311},4,"visitorIsMaintainer",false,"routes/_public/mcp/servers/~namespace/~slug/_pages/reviews/_route",{"_68":-5,"_29":316,"_231":359,"_393":-5},{"_71":72,"_73":317,"_13":321,"_96":32,"_97":32,"_98":322,"_102":323,"_125":-5,"_16":126,"_127":128,"_129":326,"_33":34,"_141":116,"_142":-5,"_143":-5,"_144":-5,"_145":-5,"_146":329,"_152":-5,"_137":112,"_153":330,"_158":331,"_117":228,"_229":230},{"_75":318,"_82":320,"_80":84},{"_77":319},{"_31":79,"_80":81},[77],[86,87,88,89,90,91,92,93,94,95],{"_35":36,"_100":101},{"_104":105,"_106":107,"_108":109,"_110":111,"_33":112,"_113":324,"_119":120,"_121":325,"_124":120},{"_115":116,"_117":118},{"_33":123},[327],{"_132":328,"_31":139,"_117":140},{"_134":135,"_33":136,"_137":138},{"_148":149,"_150":149,"_151":149},[155,156,157],[332,345],{"_33":161,"_31":162,"_163":333},{"_80":84,"_165":166,"_82":334,"_75":335},[168,169,170],{"_172":336,"_169":337,"_186":339,"_170":340,"_168":342,"_203":344},{"_165":174,"_175":176,"_31":177},{"_165":179,"_180":338,"_31":185},[182,183,184],{"_165":188,"_175":-5,"_31":189},{"_165":191,"_180":341,"_31":196},[193,194,195],{"_165":198,"_180":343,"_31":202},[200,201],{"_165":205,"_175":-5,"_31":206},{"_33":208,"_31":209,"_163":346},{"_80":84,"_165":211,"_82":347,"_75":348},[213,169,170],{"_172":349,"_169":350,"_186":352,"_170":353,"_203":355,"_213":356},{"_165":174,"_175":176,"_31":177},{"_165":179,"_180":351,"_31":185},[182,183,184],{"_165":188,"_175":-5,"_31":189},{"_165":191,"_180":354,"_31":196},[193,194,195],{"_165":205,"_175":-5,"_31":206},{"_80":223,"_224":357,"_165":227},{"_180":358,"_31":202},[200,201],[360,362,364,366,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382],{"_165":361},"Reviews | Atla | Glama",{"_236":363,"_33":31},"Read community reviews and feedback for Atla. Discover user experiences, ratings, strengths, and potential limitations of this MCP implementation from actual users.",{"_238":365,"_240":241,"_242":243},"https://glama.ai/mcp/servers/@atla-ai/atla-mcp-server/reviews",{"_236":367,"_245":246},"Reviews | Atla",{"_236":363,"_245":248},{"_236":250,"_245":251},{"_236":253,"_245":254},{"_236":256,"_245":257},{"_236":259,"_245":260},{"_236":262,"_245":263},{"_236":365,"_245":265},{"_236":267,"_245":268},{"_236":270,"_33":271},{"_236":267,"_33":273},{"_236":363,"_33":275},{"_236":253,"_33":277},{"_236":256,"_33":279},{"_236":259,"_33":281},{"_283":383},{"_285":286,"_287":384},[385],{"_285":290,"_291":292,"_293":386},[387,388,389,390,391],{"_291":296,"_297":298,"_33":299,"_300":301},{"_291":296,"_297":303,"_33":304,"_300":305},{"_291":296,"_297":307,"_33":308,"_300":309},{"_291":296,"_297":239,"_33":34,"_300":311},{"_291":296,"_297":365,"_33":392,"_300":120},"Reviews","review","actionData","errors"]

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