Use Silicon Analysts with LangChain

pip install langchain langgraph langchain-mcp-adapters langchain-anthropic

# load_silicon_analysts_tools.py
import asyncio
import os
from langchain_mcp_adapters.client import MultiServerMCPClient

async def main():
    client = MultiServerMCPClient({
        "silicon-analysts": {
            "transport": "streamable_http",
            "url": "https://siliconanalysts.com/api/mcp",
            "headers": {
                "Authorization": f"Bearer {os.environ['SA_API_KEY']}",
            },
        }
    })

    tools = await client.get_tools()
    for t in tools:
        print(t.name, "—", t.description[:80])

asyncio.run(main())

# react_agent.py
import asyncio
import os
from langchain_anthropic import ChatAnthropic
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

SYSTEM_PROMPT = (
    "You are a semiconductor cost analyst. When asked about chip economics, "
    "wafer prices, packaging, or HBM, use the silicon-analysts tools to "
    "ground your answers in current data. Always cite the "
    "provenance.last_updated field returned by each tool."
)

async def main():
    client = MultiServerMCPClient({
        "silicon-analysts": {
            "transport": "streamable_http",
            "url": "https://siliconanalysts.com/api/mcp",
            "headers": {
                "Authorization": f"Bearer {os.environ['SA_API_KEY']}",
            },
        }
    })

    tools = await client.get_tools()
    model = ChatAnthropic(model="claude-sonnet-4-5")

    agent = create_react_agent(
        model=model,
        tools=tools,
        prompt=SYSTEM_PROMPT,
    )

    result = await agent.ainvoke({
        "messages": [{
            "role": "user",
            "content": (
                "Compare the manufacturing cost breakdowns of NVIDIA H100 "
                "and B200. Which has higher gross margins? Cite freshness."
            ),
        }]
    })

    print(result["messages"][-1].content)

asyncio.run(main())

from langchain_core.tools import tool

@tool
def lookup_internal_bom(part_number: str) -> str:
    """Look up our internal BOM database by part number."""
    return query_my_db(part_number)

mcp_tools = await client.get_tools()
all_tools = mcp_tools + [lookup_internal_bom]

agent = create_react_agent(model=model, tools=all_tools, prompt=SYSTEM_PROMPT)