docs: lesson 7 - mcp

app/public/characters.json

@@ -43,5 +43,23 @@
         "image": "ada.jpeg",
         "avatar": "ada-avatar.jpeg",
         "voice": 2
+    },
+    {
+        "title": "Scipio Africanus",
+        "name": "scipio",
+        "description": "You are Scipio Africanus, a Roman general known for defeating Hannibal in the Second Punic War. Limit your responses to only the time you live in, you don't know anything else. You only want to talk about military strategy and possibly new ideas you have.",
+        "page": "scipio.html",
+        "image": "scipio.png",
+        "avatar": "scipio-avatar.jpeg",
+        "voice": 1
+    },
+    {
+        "title": "Hedy Lamarr",
+        "name": "hedy",
+        "description": "You are Hedy Lamarr, a famous actress and inventor. Limit your responses to only the time you live in, you don't know anything else. You only want to talk about your inventions and possibly new ideas you have.",
+        "page": "hedy.html",
+        "image": "hedy.jpeg",
+        "avatar": "hedy-avatar.jpeg",
+        "voice": 2
     }
 ]

lessons/07-mcp/code/build/client.js

@@ -0,0 +1,82 @@
+import { Client } from "@modelcontextprotocol/sdk/client/index.js";
+import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js";
+import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
+import { OpenAI } from 'openai';
+const transport = new StdioClientTransport({
+    command: "node",
+    args: ["build/index.js"]
+});
+const client = new Client({
+    name: "example-client",
+    version: "1.0.0"
+});
+await client.connect(transport);
+function toToolSchema(method, schema) {
+    return {
+        name: method,
+        description: `This is a tool that does ${method}`,
+        parameters: schema,
+    };
+}
+// list tools
+const { tools } = await client.listTools();
+tools.forEach((tool) => {
+    console.log(`Tool: ${tool.name}`);
+    console.log(`Description: ${tool.description}`);
+    console.log(`Input schema: ${JSON.stringify(tool.inputSchema)}`);
+});
+const toolsForLLM = tools.map((tool) => {
+    return toToolSchema(tool.name, tool.inputSchema);
+});
+console.log("Schema for tool", JSON.stringify(toolsForLLM, null, 2));
+// TODO feed this to an llm as functions
+const openai = new OpenAI({
+    baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference
+    apiKey: process.env.GITHUB_TOKEN,
+});
+const messages = [
+    {
+        role: "system",
+        content: `You are a helpful assistant. You can call functions to perform tasks. Make sure to parse the function call and arguments correctly.`
+    }, {
+        role: "user",
+        content: "Add 5 and 10",
+        name: "example-user" // Adding the required 'name' property for user role
+    }
+];
+openai.chat.completions.create({
+    model: 'gpt-4o',
+    messages: messages,
+    functions: toolsForLLM
+}).then((result) => {
+    console.log("Result", result.choices[0].message);
+}).catch((error) => {
+    console.error("Error:", error);
+});
+const result = await openai.chat.completions.create({
+    model: 'gpt-4o',
+    messages: messages,
+    functions: toolsForLLM
+});
+for (const choice of result.choices) {
+    // console.log("Result", choice.message);
+    let functionCall = choice.message?.function_call;
+    let functionName = functionCall?.name;
+    console.log("Function call: ", functionName);
+    let args = functionCall?.arguments;
+    await client.callTool({
+        name: functionName ?? "",
+        arguments: typeof args === "string" ? JSON.parse(args) : args ?? {}
+    }, CallToolResultSchema).then((result) => {
+        console.log("Result from tool: ", result);
+    });
+}
+// call tool
+// const result = await client.callTool({
+//   name: "add",
+//   arguments: {
+//     a: 5,
+//     b: 10
+//   }
+// });
+// console.log(`Result: ${JSON.stringify(result)}`);

lessons/07-mcp/code/build/index-with-llm.js

@@ -0,0 +1 @@
+export {};

lessons/07-mcp/code/build/index.js

@@ -0,0 +1,38 @@
+// simple mc server
+import { McpServer, ResourceTemplate } from "@modelcontextprotocol/sdk/server/mcp.js";
+import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
+import { z } from "zod";
+// Create an MCP server
+const server = new McpServer({
+    name: "Demo",
+    version: "1.0.0"
+});
+// Add an addition tool
+// @ts-ignore
+server.tool("add", {
+    input: z.object({
+        a: z.number(),
+        b: z.number()
+    }),
+    output: z.object({
+        content: z.array(z.object({ type: z.string(), text: z.string() }))
+    })
+}, async ({ input }) => ({
+    content: [{ type: "text", text: String(input.a + input.b) }]
+}));
+// Add a dynamic greeting resource
+server.resource("greeting", new ResourceTemplate("greeting://{name}", { list: undefined }), async (uri, { name }) => ({
+    contents: [{
+            uri: uri.href,
+            text: `Hello, ${name}!`
+        }]
+}));
+// Start receiving messages on stdin and sending messages on stdout
+const transport = new StdioServerTransport();
+async function runServer() {
+    await server.connect(transport);
+}
+runServer().catch((err) => {
+    console.error("Error in server:", err);
+    process.exit(1);
+});