microsoft-foundry/mcp-foundry 是从 https://github.com/microsoft-foundry/mcp-foundry. 导入的 MCP 服务器候选
概览
microsoft-foundry/mcp-foundry 是从 https://github.com/microsoft-foundry/mcp-foundry. 导入的 MCP 服务器候选
概述（AI 翻译）
Azure AI Foundry 的模型上下文协议服务器，提供一套统一的工具，用于模型、知识、评估等。
重要通知
Azure AI Foundry 的 MCP 服务器（实验阶段）已迁移至云端，现为 Foundry MCP 服务器（预览版）。请查看官方公开文档“开始使用 Foundry MCP 服务器（预览版）”。该 GitHub 仓库包含之前实验性服务器实现的过时信息，不会进一步更新。请推荐新的远程托管和管理 Foundry MCP 服务器，其优势如下：
- 云托管的AI工具编排界面：Foundry MCP Server（预览版）为符合MCP的客户端提供安全、可扩展的端点。无需部署基础设施，实现无缝集成和多代理场景。
- 身份与访问控制：服务器通过 Microsoft Entra ID 强制认证和授权。它执行所有操作，均在被认证用户权限范围内（代表流程）。
本页由 AI 翻译，技术细节请以原文为准。
README
以下为原文 README 节选，完整内容请查看原文。
MCP Server that interacts with Azure AI Foundry (experimental)
A Model Context Protocol server for Azure AI Foundry, providing a unified set of tools for models, knowledge, evaluation, and more.
Important notice
MCP Server for Azure AI Foundry (experimental) has moved to the cloud, now as Foundry MCP Server (preview). Please check the official public documentation at Get started with Foundry MCP Server (preview). This GitHub repository contains the outdated information on the previous experimental server implementation, and will not be updated further. Instead, refer to the new, remotely hosted and managed Foundry MCP Server, that has the following benefits:
- Cloud-hosted interface for AI tool orchestration: Foundry MCP Server (preview) provides a secure, scalable endpoint for MCP-compliant clients. You don’t need to deploy infrastructure, enabling seamless integration and multi-agent scenarios.
- Identity and access control: The server enforces authentication and authorization with Microsoft Entra ID. It performs all operations within the authenticated user’s permissions (On-Behalf-Of flow).
- Scenario-focused, extensible tools: The MCP Server exposes a growing set of tools for read and write operations on models, deployments, evaluations, and agents in Microsoft Foundry. The tools are extensible, letting developers and agents interact with services without knowing backend APIs or data schemas.
- Accelerated agent and developer productivity: Natural language workflows (via MCP clients and large language models) enable rapid tool discovery and invocation, streamlining development and multi-agent orchestration.
The rest of this repo (including codebase) explains the now-deprecated MCP Server that interactes with Azure AI Foundry (experimental).
Available Tools
Capabilities: Models
| Category | Tool | Description |
|---|---|---|
| Explore | list_models_from_model_catalog |
Retrieves a list of supported models from the Azure AI Foundry catalog. |
list_azure_ai_foundry_labs_projects |
Retrieves a list of state-of-the-art AI models from Microsoft Research available in Azure AI Foundry Labs. |
安装
uvx --prerelease=allow --from git+https://github.com/azure-ai-foundry/mcp-foundry.git run-azure-ai-foundry-mcp --envFile ${workspaceFolder}/.env