Add Microsoft Learn MCP integration and update ResearcherAgent to sup…

.vscode/mcp.json

@@ -8,6 +8,11 @@
         "MAF_DOCTOR_INIT_VERSION": "1.14.0",
         "MAF_DOCTOR_WORKSPACE_ROOTS": "C:\\Users\\jesseliberty\\ai\\.net\\blogWriter;E:\\ai\\.net\\blog\\blogMigration---public"
       }
+    },
+    "microsoft-learn": {
+      "type": "http",
+      "url": "https://learn.microsoft.com/api/mcp"
     }
   }
 }
+

BlogWriter.csproj

@@ -28,6 +28,7 @@
     <PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.10" />
     <PackageReference Include="Microsoft.Extensions.Logging" Version="10.0.10" />
     <PackageReference Include="Microsoft.Extensions.Logging.Console" Version="10.0.10" />
+    <PackageReference Include="ModelContextProtocol" Version="2.2.0" />
     <PackageReference Include="OpenAI" Version="2.11.0" />
   </ItemGroup>
 

Program.cs

@@ -6,6 +6,7 @@
 using Microsoft.Extensions.AI;
 using Microsoft.Extensions.Configuration;
 using Microsoft.Extensions.Logging;
+using ModelContextProtocol.Client;
 using OpenAI;
 
 // Secrets come from the .NET user-secrets store and from
@@ -115,11 +116,23 @@ async Task<HttpResponseMessage> PostWithRetryAsync(string requestUri, object bod
     name: "tavily_search",
     description: "A search engine optimized for comprehensive, accurate, and trusted results.");
 
+// Microsoft Learn's remote MCP server exposes docs search/fetch tools the
+// Researcher can call alongside Tavily for authoritative Microsoft/Azure content.
+await using McpClient microsoftLearnMcp = await McpClient.CreateAsync(
+    new HttpClientTransport(new HttpClientTransportOptions
+    {
+        Endpoint = new Uri("https://learn.microsoft.com/api/mcp"),
+        Name = "microsoft-learn",
+    }));
+IList<McpClientTool> microsoftLearnTools = await microsoftLearnMcp.ListToolsAsync();
+
+List<AIFunction> researcherTools = [tavilyTool, .. microsoftLearnTools];
+
 // Creating a callable object
 using ILoggerFactory loggerFactory = LoggerFactory.Create(builder => builder.AddConsole());
 
 var bloggerAgent = new BloggerAgent(llm, chatOptions, loggerFactory.CreateLogger<BloggerAgent>());
-var researcherAgent = new ResearcherAgent(llm, chatOptions, tavilyTool, loggerFactory.CreateLogger<ResearcherAgent>());
+var researcherAgent = new ResearcherAgent(llm, chatOptions, researcherTools, loggerFactory.CreateLogger<ResearcherAgent>());
 var authorAgent = new AuthorAgent(llm, chatOptions, loggerFactory.CreateLogger<AuthorAgent>());
 var reviewerAgent = new ReviewerAgent(llm, chatOptions, loggerFactory.CreateLogger<ReviewerAgent>());
 var app = new BlogWorkflow(bloggerAgent, researcherAgent, authorAgent, reviewerAgent, loggerFactory.CreateLogger<BlogWorkflow>());

ResearcherAgent.cs

@@ -9,8 +9,9 @@ namespace BlogWriter;
 /// Performs research tasks with a <see cref="ChatClientAgent"/> and returns
 /// concise findings.
 ///
-/// The agent can call the configured Tavily tool during execution and summarize
-/// results for use in later drafting stages.
+/// The agent can call the configured tools (e.g. Tavily web search, Microsoft
+/// Learn MCP) during execution and summarize results for use in later drafting
+/// stages.
 /// </summary>
 public class ResearcherAgent : IResearcherAgent
 {
@@ -28,11 +29,13 @@ public class ResearcherAgent : IResearcherAgent
     // Per-call output-token cap, applied on each RunAsync to bound cost.
     private readonly int? _maxOutputTokens;
 
-    public ResearcherAgent(IChatClient llm, ChatOptions chatOptions, AIFunction tavilyTool, ILogger<ResearcherAgent> logger)
+    public ResearcherAgent(IChatClient llm, ChatOptions chatOptions, IEnumerable<AIFunction> tools, ILogger<ResearcherAgent> logger)
     {
         _logger = logger;
         _maxOutputTokens = chatOptions.MaxOutputTokens;
 
+        List<AITool> toolList = [.. tools];
+
         _agent = new ChatClientAgent(llm, new ChatClientAgentOptions
         {
             // Name surfaces in OpenTelemetry traces and agent logs.
@@ -45,29 +48,28 @@ public ResearcherAgent(IChatClient llm, ChatOptions chatOptions, AIFunction tavi
                 // Preserve the original sampling/cost settings.
                 Temperature = chatOptions.Temperature,
                 MaxOutputTokens = chatOptions.MaxOutputTokens,
-                // Attaching the tool lets the model call it autonomously.
-                Tools = [tavilyTool],
+                // Attaching the tools lets the model call them autonomously.
+                Tools = toolList,
             },
         })
         .AsBuilder()
         // Function-invocation middleware: fires around every tool call the agent
-        // makes. We log each time the model invokes the Tavily search tool.
+        // makes. We log each time the model invokes one of the attached tools.
         .Use(async (agent, context, next, cancellationToken) =>
         {
-            if (context.Function.Name == tavilyTool.Name)
-            {
-                _logger.LogInformation(
-                    "Researcher invoking Tavily tool '{Tool}' with arguments {Arguments}",
-                    context.Function.Name,
-                    context.Arguments);
-            }
+            _logger.LogInformation(
+                "Researcher invoking tool '{Tool}' with arguments {Arguments}",
+                context.Function.Name,
+                context.Arguments);
 
             return await next(context, cancellationToken);
         })
         .UseOpenTelemetry(sourceName: "BlogWriter.Agents")
         .Build();
 
-        _logger.LogInformation("ResearcherAgent initialized with Tavily tool: {ToolName}", tavilyTool.Name);
+        _logger.LogInformation(
+            "ResearcherAgent initialized with tools: {ToolNames}",
+            string.Join(", ", toolList.Select(t => t.Name)));
     }
 
     /// <summary>Execute research by letting the agent search and summarise.</summary>