// Copyright (c) Microsoft. All rights reserved.

// This sample shows how to create and use a simple AI agent with Azure Foundry Agents as the backend, that uses a Hosted MCP Tool.
// In this case the Azure Foundry Agents service will invoke any MCP tools as required. MCP tools are not invoked by the Agent Framework.
// The sample first shows how to use MCP tools with auto approval, and then how to set up a tool that requires approval before it can be invoked and how to approve such a tool.

using Azure.AI.Agents.Persistent;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;

var endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
var model = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4.1-mini";

// Get a client to create/retrieve server side agents with.
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
var persistentAgentsClient = new PersistentAgentsClient(endpoint, new DefaultAzureCredential());

// **** MCP Tool with Auto Approval ****
// *************************************

// Create an MCP tool definition that the agent can use.
// In this case we allow the tool to always be called without approval.
var mcpTool = new HostedMcpServerTool(
    serverName: "microsoft_learn",
    serverAddress: "https://learn.microsoft.com/api/mcp")
{
    AllowedTools = ["microsoft_docs_search"],
    ApprovalMode = HostedMcpServerToolApprovalMode.NeverRequire
};

// Create a server side persistent agent with the mcp tool, and expose it as an AIAgent.
AIAgent agent = await persistentAgentsClient.CreateAIAgentAsync(
    model: model,
    options: new()
    {
        Name = "MicrosoftLearnAgent",
        ChatOptions = new()
        {
            Instructions = "You answer questions by searching the Microsoft Learn content only.",
            Tools = [mcpTool]
        },
    });

// You can then invoke the agent like any other AIAgent.
AgentSession session = await agent.CreateSessionAsync();
Console.WriteLine(await agent.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", session));

// Cleanup for sample purposes.
await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);

// **** MCP Tool with Approval Required ****
// *****************************************

// Create an MCP tool definition that the agent can use.
// In this case we require approval before the tool can be called.
var mcpToolWithApproval = new HostedMcpServerTool(
    serverName: "microsoft_learn",
    serverAddress: "https://learn.microsoft.com/api/mcp")
{
    AllowedTools = ["microsoft_docs_search"],
    ApprovalMode = HostedMcpServerToolApprovalMode.AlwaysRequire
};

// Create an agent based on Azure OpenAI Responses as the backend.
AIAgent agentWithRequiredApproval = await persistentAgentsClient.CreateAIAgentAsync(
    model: model,
    options: new()
    {
        Name = "MicrosoftLearnAgentWithApproval",
        ChatOptions = new()
        {
            Instructions = "You answer questions by searching the Microsoft Learn content only.",
            Tools = [mcpToolWithApproval]
        },
    });

// You can then invoke the agent like any other AIAgent.
// For simplicity, we are assuming here that only mcp tool approvals are pending.
AgentSession sessionWithRequiredApproval = await agentWithRequiredApproval.CreateSessionAsync();
AgentResponse response = await agentWithRequiredApproval.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", sessionWithRequiredApproval);
List<McpServerToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<McpServerToolApprovalRequestContent>().ToList();

while (approvalRequests.Count > 0)
{
    // Ask the user to approve each MCP call request.
    List<ChatMessage> userInputResponses = approvalRequests
        .ConvertAll(approvalRequest =>
        {
            Console.WriteLine($"""
                The agent would like to invoke the following MCP Tool, please reply Y to approve.
                ServerName: {approvalRequest.ToolCall.ServerName}
                Name: {approvalRequest.ToolCall.ToolName}
                Arguments: {string.Join(", ", approvalRequest.ToolCall.Arguments?.Select(x => $"{x.Key}: {x.Value}") ?? [])}
                """);
            return new ChatMessage(ChatRole.User, [approvalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
        });

    // Pass the user input responses back to the agent for further processing.
    response = await agentWithRequiredApproval.RunAsync(userInputResponses, sessionWithRequiredApproval);

    approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<McpServerToolApprovalRequestContent>().ToList();
}

Console.WriteLine($"\nAgent: {response}");