reinstate tiny agents section as bonus

Author: burtenshaw

units/en/unit2/tiny-agents.mdx

@@ -174,3 +174,384 @@ The complete flow we've built allows an agent to:
 - Combine it with other capabilities like file system access and web browsing
 
 This modular approach is what makes MCP so powerful for building flexible AI applications.
+
+## Bonus: Build a Browser Automation MCP Server with Playwright
+
+As a bonus, let's explore how to use the Playwright MCP server for browser automation with Tiny Agents. This demonstrates the extensibility of the MCP ecosystem beyond our sentiment analysis example.
+
+<Tip>
+
+This section is based on the [Tiny Agents blog post](https://huggingface.co/blog/tiny-agents) and adapted for the MCP course. 
+
+</Tip>
+
+In this section, we'll show you how to build an agent that can perform web automation tasks like searching, clicking, and extracting information from websites.
+
+```ts
+// playwright-agent.ts
+import { Agent } from "@huggingface/tiny-agents";
+
+const agent = new Agent({
+  provider: process.env.PROVIDER ?? "nebius",
+  model: process.env.MODEL_ID ?? "Qwen/Qwen2.5-72B-Instruct",
+  apiKey: process.env.HF_TOKEN,
+  servers: [
+    {
+      command: "npx",
+      args: ["playwright-mcp"]
+    }
+  ],
+});
+
+await agent.run();
+```
+
+The Playwright MCP server exposes tools that allow your agent to:
+
+1. Open browser tabs
+2. Navigate to URLs
+3. Click on elements
+4. Type into forms
+5. Extract content from webpages
+6. Take screenshots
+
+Here's an example interaction with our browser automation agent:
+
+```
+User: Search for "tiny agents" on GitHub and collect the names of the top 3 repositories
+
+Agent: I'll search GitHub for "tiny agents" repositories.
+[Agent opens browser, navigates to GitHub, performs the search, and extracts repository names]
+
+Here are the top 3 (not real) repositories for "tiny agents":
+1. huggingface/tiny-agents
+2. modelcontextprotocol/tiny-agents-examples
+3. langchain/tiny-agents-js
+```
+
+This browser automation capability can be combined with other MCP servers to create powerful workflows—for example, extracting text from a webpage and then analyzing it with custom tools.
+
+## How to run the complete demo
+
+If you have NodeJS (with `pnpm` or `npm`), just run this in a terminal:
+
+```bash
+npx @huggingface/mcp-client
+```
+
+or if using `pnpm`:
+
+```bash
+pnpx @huggingface/mcp-client
+```
+
+This installs the package into a temporary folder then executes its command.
+
+You'll see your simple Agent connect to multiple MCP servers (running locally), loading their tools (similar to how it would load your Gradio sentiment analysis tool), then prompting you for a conversation.
+
+<video controls autoplay loop>
+  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/tiny-agents/use-filesystem.mp4" type="video/mp4">
+</video>
+
+By default our example Agent connects to the following two MCP servers:
+
+- the "canonical" [file system server](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem), which gets access to your Desktop,
+- and the [Playwright MCP](https://github.com/microsoft/playwright-mcp) server, which knows how to use a sandboxed Chromium browser for you.
+
+> [!NOTE]
+> Note: this is a bit counter-intuitive but currently, all MCP servers in tiny agents are actually local processes (though remote servers are coming soon). 
+
+Our input for this first video was:
+
+> write a haiku about the Hugging Face community and write it to a file named "hf.txt" on my Desktop
+
+Now let us try this prompt that involves some Web browsing:
+
+> do a Web Search for HF inference providers on Brave Search and open the first 3 results
+
+<video controls autoplay loop>
+  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/tiny-agents/brave-search.mp4" type="video/mp4">
+</video>
+
+### Default model and provider
+
+In terms of model/provider pair, our example Agent uses by default:
+- ["Qwen/Qwen2.5-72B-Instruct"](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
+- running on [Nebius](https://huggingface.co/docs/inference-providers/providers/nebius)
+
+This is all configurable through env variables:
+
+```ts
+const agent = new Agent({
+	provider: process.env.PROVIDER ?? "nebius",
+	model: process.env.MODEL_ID ?? "Qwen/Qwen2.5-72B-Instruct",
+	apiKey: process.env.HF_TOKEN,
+	servers: [
+		// Default servers
+		{
+			command: "npx",
+			args: ["@modelcontextprotocol/servers", "filesystem"]
+		},
+		{
+			command: "npx",
+			args: ["playwright-mcp"]
+		},
+	],
+});
+```
+
+## Implementing an MCP client on top of InferenceClient
+
+Now that we know what a tool is in recent LLMs, let's implement the actual MCP client that will communicate with MCP servers and other MCP servers.
+
+The official doc at https://modelcontextprotocol.io/quickstart/client is fairly well-written. You only have to replace any mention of the Anthropic client SDK by any other OpenAI-compatible client SDK. (There is also a [llms.txt](https://modelcontextprotocol.io/llms-full.txt) you can feed into your LLM of choice to help you code along). 
+
+As a reminder, we use HF's `InferenceClient` for our inference client.
+
+> [!TIP]
+> The complete `McpClient.ts` code file is [here](https://github.com/huggingface/huggingface.js/blob/main/packages/mcp-client/src/McpClient.ts) if you want to follow along using the actual code 🤓
+
+Our `McpClient` class has:
+- an Inference Client (works with any Inference Provider, and `huggingface/inference` supports both remote and local endpoints)
+- a set of MCP client sessions, one for each connected MCP server (this allows us to connect to multiple servers)
+- and a list of available tools that is going to be filled from the connected servers and just slightly re-formatted.
+
+```ts
+export class McpClient {
+	protected client: InferenceClient;
+	protected provider: string;
+	protected model: string;
+	private clients: Map<ToolName, Client> = new Map();
+	public readonly availableTools: ChatCompletionInputTool[] = [];
+
+	constructor({ provider, model, apiKey }: { provider: InferenceProvider; model: string; apiKey: string }) {
+		this.client = new InferenceClient(apiKey);
+		this.provider = provider;
+		this.model = model;
+	}
+	
+	// [...]
+}
+```
+
+To connect to a MCP server, the official `@modelcontextprotocol/sdk/client` TypeScript SDK provides a `Client` class with a `listTools()` method:
+
+```ts
+async addMcpServer(server: StdioServerParameters): Promise<void> {
+	const transport = new StdioClientTransport({
+		...server,
+		env: { ...server.env, PATH: process.env.PATH ?? "" },
+	});
+	const mcp = new Client({ name: "@huggingface/mcp-client", version: packageVersion });
+	await mcp.connect(transport);
+
+	const toolsResult = await mcp.listTools();
+	debug(
+		"Connected to server with tools:",
+		toolsResult.tools.map(({ name }) => name)
+	);
+
+	for (const tool of toolsResult.tools) {
+		this.clients.set(tool.name, mcp);
+	}
+
+	this.availableTools.push(
+		...toolsResult.tools.map((tool) => {
+			return {
+				type: "function",
+				function: {
+					name: tool.name,
+					description: tool.description,
+					parameters: tool.inputSchema,
+				},
+			} satisfies ChatCompletionInputTool;
+		})
+	);
+}
+```
+
+`StdioServerParameters` is an interface from the MCP SDK that will let you easily spawn a local process: as we mentioned earlier, currently, all MCP servers are actually local processes.
+
+### How to use the tools
+
+Using our sentiment analysis tool (or any other MCP tool) is straightforward. You just pass `this.availableTools` to your LLM chat-completion, in addition to your usual array of messages:
+
+```ts
+const stream = this.client.chatCompletionStream({
+	provider: this.provider,
+	model: this.model,
+	messages,
+	tools: this.availableTools,
+	tool_choice: "auto",
+});
+```
+
+`tool_choice: "auto"` is the parameter you pass for the LLM to generate zero, one, or multiple tool calls.
+
+When parsing or streaming the output, the LLM will generate some tool calls (i.e. a function name, and some JSON-encoded arguments), which you (as a developer) need to compute. The MCP client SDK once again makes that very easy; it has a `client.callTool()` method:
+
+```ts
+const toolName = toolCall.function.name;
+const toolArgs = JSON.parse(toolCall.function.arguments);
+
+const toolMessage: ChatCompletionInputMessageTool = {
+	role: "tool",
+	tool_call_id: toolCall.id,
+	content: "",
+	name: toolName,
+};
+
+/// Get the appropriate session for this tool
+const client = this.clients.get(toolName);
+if (client) {
+	const result = await client.callTool({ name: toolName, arguments: toolArgs });
+	toolMessage.content = result.content[0].text;
+} else {
+	toolMessage.content = `Error: No session found for tool: ${toolName}`;
+}
+```
+
+If the LLM chooses to use a tool, this code will automatically route the call to the MCP server, execute the analysis, and return the result back to the LLM.
+
+Finally you will add the resulting tool message to your `messages` array and back into the LLM.
+
+## Our 50-lines-of-code Agent 🤯
+
+Now that we have an MCP client capable of connecting to arbitrary MCP servers  to get lists of tools and capable of injecting them and parsing them from the LLM inference, well... what is an Agent?
+
+> Once you have an inference client with a set of tools, then an Agent is just a while loop on top of it.
+
+In more detail, an Agent is simply a combination of:
+- a system prompt
+- an LLM Inference client
+- an MCP client to hook a set of Tools into it from a bunch of MCP servers 
+- some basic control flow (see below for the while loop)
+
+> [!TIP]
+> The complete `Agent.ts` code file is [here](https://github.com/huggingface/huggingface.js/blob/main/packages/mcp-client/src/Agent.ts).
+
+Our Agent class simply extends McpClient:
+
+```ts
+export class Agent extends McpClient {
+	private readonly servers: StdioServerParameters[];
+	protected messages: ChatCompletionInputMessage[];
+
+	constructor({
+		provider,
+		model,
+		apiKey,
+		servers,
+		prompt,
+	}: {
+		provider: InferenceProvider;
+		model: string;
+		apiKey: string;
+		servers: StdioServerParameters[];
+		prompt?: string;
+	}) {
+		super({ provider, model, apiKey });
+		this.servers = servers;
+		this.messages = [
+			{
+				role: "system",
+				content: prompt ?? DEFAULT_SYSTEM_PROMPT,
+			},
+		];
+	}
+}
+```
+
+By default, we use a very simple system prompt inspired by the one shared in the [GPT-4.1 prompting guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide).
+
+Even though this comes from OpenAI 😈, this sentence in particular applies to more and more models, both closed and open:
+
+> We encourage developers to exclusively use the tools field to pass tools, rather than manually injecting tool descriptions into your prompt and writing a separate parser for tool calls, as some have reported doing in the past.
+
+Which is to say, we don't need to provide painstakingly formatted lists of tool use examples in the prompt. The `tools: this.availableTools` param is enough, and the LLM will know how to use both the filesystem tools.
+
+Loading the tools on the Agent is literally just connecting to the MCP servers we want (in parallel because it's so easy to do in JS):
+
+```ts
+async loadTools(): Promise<void> {
+	await Promise.all(this.servers.map((s) => this.addMcpServer(s)));
+}
+```
+
+We add two extra tools (outside of MCP) that can be used by the LLM for our Agent's control flow:
+
+```ts
+const taskCompletionTool: ChatCompletionInputTool = {
+	type: "function",
+	function: {
+		name: "task_complete",
+		description: "Call this tool when the task given by the user is complete",
+		parameters: {
+			type: "object",
+			properties: {},
+		},
+	},
+};
+const askQuestionTool: ChatCompletionInputTool = {
+	type: "function",
+	function: {
+		name: "ask_question",
+		description: "Ask a question to the user to get more info required to solve or clarify their problem.",
+		parameters: {
+			type: "object",
+			properties: {},
+		},
+	},
+};
+const exitLoopTools = [taskCompletionTool, askQuestionTool];
+```
+
+When calling any of these tools, the Agent will break its loop and give control back to the user for new input.
+
+### The complete while loop
+
+Behold our complete while loop.🎉
+
+The gist of our Agent's main while loop is that we simply iterate with the LLM alternating between tool calling and feeding it the tool results, and we do so **until the LLM starts to respond with two non-tool messages in a row**.
+
+This is the complete while loop:
+
+```ts
+let numOfTurns = 0;
+let nextTurnShouldCallTools = true;
+while (true) {
+	try {
+		yield* this.processSingleTurnWithTools(this.messages, {
+			exitLoopTools,
+			exitIfFirstChunkNoTool: numOfTurns > 0 && nextTurnShouldCallTools,
+			abortSignal: opts.abortSignal,
+		});
+	} catch (err) {
+		if (err instanceof Error && err.message === "AbortError") {
+			return;
+		}
+		throw err;
+	}
+	numOfTurns++;
+	const currentLast = this.messages.at(-1)!;
+	if (
+		currentLast.role === "tool" &&
+		currentLast.name &&
+		exitLoopTools.map((t) => t.function.name).includes(currentLast.name)
+	) {
+		return;
+	}
+	if (currentLast.role !== "tool" && numOfTurns > MAX_NUM_TURNS) {
+		return;
+	}
+	if (currentLast.role !== "tool" && nextTurnShouldCallTools) {
+		return;
+	}
+	if (currentLast.role === "tool") {
+		nextTurnShouldCallTools = false;
+	} else {
+		nextTurnShouldCallTools = true;
+	}
+}
+```
+