How do I use UpRock as a LangChain tool?

# pip install "langchain[mcp]" langchain-anthropic   env: UPROCK_API_KEY, ANTHROPIC_API_KEYimport asyncioimport jsonimport os​from langchain.agents import create_agentfrom fastmcp.client.transports import StreamableHttpTransportfrom langchain.mcp import MCPAdapter  # in beta as of LangChain 1.4​UPROCK = StreamableHttpTransport(    "https://mcp.uprock.ai/mcp",    headers={"Authorization": f"Bearer {os.environ['UPROCK_API_KEY']}"},)MODEL = "anthropic:claude-sonnet-5-5"  # any chat model LangChain supportsSYSTEM = (    "When you need a web page as it looks in a specific country, or another fetch failed, "    "call crawl_fetch with the url and country, then resource_fetch on the markdown resource "    "it returns to read the full page.")​​async def main() -> None:    async with MCPAdapter(UPROCK) as uprock:        tools = (            await uprock.list_tools()        )  # crawl_fetch, resource_fetch, video_search, ...        by_name = {tool.name: tool for tool in tools}​        # 1. A direct call, no model involved: load a page on a device in Germany.        blocks = await by_name["crawl_fetch"].ainvoke(            {"url": "https://example.com", "country": "DE"}        )        page = json.loads(            blocks[0]["text"]        )  # a JSON summary, then crawl:// resource links        uri = page["content"]["markdown"]["resource"]        markdown = await by_name["resource_fetch"].ainvoke({"uri": uri})        print(page["meta"]["title"], len(markdown[0]["text"]), "chars of markdown")​        # 2. The same tools in an agent.        agent = create_agent(MODEL, tools, system_prompt=SYSTEM)        question = "What does https://example.com say when opened from Germany?"        result = await agent.ainvoke(            {"messages": [{"role": "user", "content": question}]}        )        print(result["messages"][-1].content)​​asyncio.run(main())

// npm i @langchain/mcp-adapters @langchain/core   (Node 20.10+)   env: UPROCK_API_KEYimport { MCPAdapter } from "@langchain/mcp-adapters";​const uprock = new MCPAdapter({  servers: {    uprock: {      url: "https://mcp.uprock.ai/mcp",      headers: { Authorization: `Bearer ${process.env.UPROCK_API_KEY}` },    },  },  defaultToolTimeout: 300_000, // a fresh fetch can take 10 to 40 seconds});​// A tool returns its content blocks, or a plain string when there's a single text block.type Block = { type: string; text?: string };const textOf = (out: unknown) =>  typeof out === "string"    ? out    : ((out as Block[]).find((b) => b.type === "text")?.text ?? "");​try {  // The adapter prefixes tool names with the server name: uprock_crawl_fetch, uprock_resource_fetch, ...  const tools = await uprock.listTools();  const tool = (name: string) => tools.find((t) => t.name === `uprock_${name}`)!;​  // A direct call, no model involved: load a page on a device in Germany.  const page = JSON.parse(    textOf(      await tool("crawl_fetch").invoke({ url: "https://example.com", country: "DE" }),    ),  );  const uri = page.content.markdown.resource;  const markdown = textOf(await tool("resource_fetch").invoke({ uri }));  console.log(page.meta.title, markdown.length, "chars of markdown");​  // For an agent, pass `tools` to createAgent from "langchain" while the adapter is open.} finally {  await uprock.close();}

{  "status": "success",  "meta": { "url": "https://example.com", "title": "Example Domain", "content_type": "text/html" },  "content": {    "markdown": { "resource": "crawl://…/markdown", "size_bytes": 1342 },    "html": { "resource": "crawl://…/html", "size_bytes": 2358 }  }}