[WIP] Refactor everything server to be more modular and use recommended APIs.

* Updated architecture.md

* Refactor/renamed get-sampling-request.ts to trigger-sampling-request.ts
  - use trigger instead of get throughout

* In tools/index.ts
  - sorted display order

src/everything/tools/index.ts

@@ -4,7 +4,6 @@ import { registerEchoTool } from "./echo.js";
 import { registerGetEnvTool } from "./get-env.js";
 import { registerGetResourceLinksTool } from "./get-resource-links.js";
 import { registerGetResourceReferenceTool } from "./get-resource-reference.js";
-import { registerGetSamplingRequestTool } from "./get-sampling-request.js";
 import { registerGetStructuredContentTool } from "./get-structured-content.js";
 import { registerGetSumTool } from "./get-sum.js";
 import { registerGetTinyImageTool } from "./get-tiny-image.js";
@@ -12,6 +11,7 @@ import { registerGZipFileAsResourceTool } from "./gzip-file-as-resource.js";
 import { registerLongRunningOperationTool } from "./long-running-operation.js";
 import { registerToggleLoggingTool } from "./toggle-logging.js";
 import { registerToggleSubscriberUpdatesTool } from "./toggle-subscriber-updates.js";
+import { registerTriggerSamplingRequestTool } from "./trigger-sampling-request.js";
 
 /**
  * Register the tools with the MCP server.
@@ -23,7 +23,6 @@ export const registerTools = (server: McpServer) => {
   registerGetEnvTool(server);
   registerGetResourceLinksTool(server);
   registerGetResourceReferenceTool(server);
-  registerGetSamplingRequestTool(server);
   registerGetStructuredContentTool(server);
   registerGetSumTool(server);
   registerGetTinyImageTool(server);
@@ -31,4 +30,5 @@ export const registerTools = (server: McpServer) => {
   registerLongRunningOperationTool(server);
   registerToggleLoggingTool(server);
   registerToggleSubscriberUpdatesTool(server);
+  registerTriggerSamplingRequestTool(server);
 };

src/everything/tools/trigger-sampling-request.ts

@@ -0,0 +1,83 @@
+import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
+import {
+  CallToolResult,
+  CreateMessageRequest,
+  CreateMessageResultSchema,
+} from "@modelcontextprotocol/sdk/types.js";
+import { z } from "zod";
+
+// Tool input schema
+const TriggerSamplingRequestSchema = z.object({
+  prompt: z.string().describe("The prompt to send to the LLM"),
+  maxTokens: z
+    .number()
+    .default(100)
+    .describe("Maximum number of tokens to generate"),
+});
+
+// Tool configuration
+const name = "trigger-sampling-request";
+const config = {
+  title: "Trigger Sampling Request Tool",
+  description: "Trigger a Request from the Server for LLM Sampling",
+  inputSchema: TriggerSamplingRequestSchema,
+};
+
+/**
+ * Registers the 'trigger-sampling-request' tool within the provided McpServer instance.
+ *
+ * The registered tool performs the following operations:
+ * - Validates incoming arguments using `SampleLLMSchema`.
+ * - Constructs a `sampling/createMessage` request object using provided prompt and maximum tokens.
+ * - Sends the request to the server for sampling.
+ * - Formats and returns the sampling result content to the client.
+ *
+ * @param {McpServer} server - The instance of the MCP server where the tool
+ *        will be registered.
+ */
+export const registerTriggerSamplingRequestTool = (server: McpServer) => {
+  server.registerTool(
+    name,
+    config,
+    async (args, extra): Promise<CallToolResult> => {
+      const validatedArgs = TriggerSamplingRequestSchema.parse(args);
+      const { prompt, maxTokens } = validatedArgs;
+
+      // Create the sampling request
+      const request: CreateMessageRequest = {
+        method: "sampling/createMessage",
+        params: {
+          messages: [
+            {
+              role: "user",
+              content: {
+                type: "text",
+                text: `Resource ${name} context: ${prompt}`,
+              },
+            },
+          ],
+          systemPrompt: "You are a helpful test server.",
+          maxTokens,
+          temperature: 0.7,
+          includeContext: "thisServer",
+        },
+      };
+
+      // Send the sampling request to the client
+      const result = await extra.sendRequest(
+        request,
+        CreateMessageResultSchema
+      );
+
+      // Return the result to the client
+      return {
+        content: [
+          {
+            type: "text",
+            text: `LLM sampling result: \n${JSON.stringify(result, null, 2)}`,
+          },
+        ],
+      };
+    }
+  );
+};