LangChain

pip install langchain-mcp-adapters langchain langchain-anthropic

import asyncio
from pathlib import Path
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent


async def main():
    client = MultiServerMCPClient({
        "xquik": {
            "transport": "http",
            "url": "https://xquik.com/mcp",
            "headers": {
                "x-api-key": "xq_YOUR_KEY_HERE",
            },
        },
    })

    tools = await client.get_tools()

    agent = create_agent(
        "anthropic:claude-sonnet-4-6",
        tools,
        system_prompt="You help users interact with X (Twitter) via the Xquik API.",
    )

    prompt = (
        "Search for the latest tweets about AI agents. Return compact JSON "
        "with query, route_used, tweets[{tweet_id,text,author_username,created_at}], "
        "has_more, next_cursor, and key influencers."
    )
    response = await agent.ainvoke({"messages": [{"role": "user", "content": prompt}]})

    Path("xquik-langchain-handoff.json").write_text(
        str(response["messages"][-1].content),
        encoding="utf-8",
    )


asyncio.run(main())

import asyncio
from pathlib import Path
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.prebuilt import ToolNode, tools_condition


async def main():
    model = init_chat_model("anthropic:claude-sonnet-4-6")

    client = MultiServerMCPClient({
        "xquik": {
            "transport": "http",
            "url": "https://xquik.com/mcp",
            "headers": {"x-api-key": "xq_YOUR_KEY_HERE"},
        },
    })

    tools = await client.get_tools()

    def call_model(state: MessagesState):
        return {"messages": model.bind_tools(tools).invoke(state["messages"])}

    builder = StateGraph(MessagesState)
    builder.add_node(call_model)
    builder.add_node(ToolNode(tools))
    builder.add_edge(START, "call_model")
    builder.add_conditional_edges("call_model", tools_condition)
    builder.add_edge("tools", "call_model")
    graph = builder.compile()

    prompt = (
        "Look up @username's public profile. Return compact JSON with "
        "username, name, user_id, description, followers_count, and route_used."
    )
    result = await graph.ainvoke({"messages": [{"role": "user", "content": prompt}]})
    Path("xquik-langgraph-handoff.json").write_text(
        str(result["messages"][-1].content),
        encoding="utf-8",
    )


asyncio.run(main())

import os
from dotenv import load_dotenv

load_dotenv()

client = MultiServerMCPClient({
    "xquik": {
        "transport": "http",
        "url": "https://xquik.com/mcp",
        "headers": {"x-api-key": os.environ["XQUIK_API_KEY"]},
    },
})

client = MultiServerMCPClient(
    {
        "xquik": {
            "transport": "http",
            "url": "https://xquik.com/mcp",
            "headers": {"x-api-key": os.environ["XQUIK_API_KEY"]},
        },
        "other_server": {
            "transport": "http",
            "url": "https://other-server.com/mcp",
        },
    },
    tool_name_prefix=True,
)