Connect Sanity Context with LangChain

Connect Sanity Context to a Python agent using LangChain and langchain-mcp-adapters.

This example connects Sanity Context to a Python agent using LangChain and langchain-mcp-adapters. The adapter library converts MCP tools to LangChain tools automatically.

You need a Sanity Context MCP endpoint. If you haven't set one up yet, start with Sanity Context. You'll need:

• MCP endpoint URL: Shown in the Sanity Context document in Studio.
• Sanity API read token: Create one at sanity.io/manage.
• Anthropic API key: ChatAnthropic reads it from ANTHROPIC_API_KEY.
• Python 3.10 or later.

pip install langchain langchain-mcp-adapters langchain-anthropic httpx python-dotenv

Create a .env file next to agent.py. load_dotenv() reads it. Replace each placeholder with your own value:

SANITY_CONTEXT_MCP_URL=YOUR_MCP_ENDPOINT_URL
SANITY_API_READ_TOKEN=YOUR_SANITY_READ_TOKEN
ANTHROPIC_API_KEY=YOUR_ANTHROPIC_API_KEY

Connect to the MCP endpoint, fetch initial context, and run the agent:

import asyncio
import os
from urllib.parse import urlparse, urlunparse

import httpx
from dotenv import load_dotenv
from langchain.agents import create_agent
from langchain_anthropic import ChatAnthropic
from langchain_mcp_adapters.client import MultiServerMCPClient

load_dotenv()

MCP_URL = os.environ["SANITY_CONTEXT_MCP_URL"]
API_TOKEN = os.environ["SANITY_API_READ_TOKEN"]


async def main():
    # 1. Fetch initial context via HTTP
    parsed = urlparse(MCP_URL)
    initial_context_url = urlunparse(
        parsed._replace(path=parsed.path.rstrip("/") + "/initial-context")
    )

    async with httpx.AsyncClient() as http:
        resp = await http.get(
            initial_context_url,
            headers={"Authorization": f"Bearer {API_TOKEN}"},
        )
        resp.raise_for_status()
        initial_context = resp.text

    # 2. Connect to Sanity Context MCP and load tools
    mcp_client = MultiServerMCPClient({
        "sanity": {
            "transport": "http",
            "url": MCP_URL,
            "headers": {"Authorization": f"Bearer {API_TOKEN}"},
        },
    })
    tools = [t for t in await mcp_client.get_tools() if t.name != "initial_context"]

    # 3. Create the agent and call the LLM
    system_prompt = (
        "You are a helpful assistant.\n\n"
        "# Data reference\n\n"
        "Use this to understand what's available and write better queries.\n\n"
        + initial_context
    )

    llm = ChatAnthropic(model="claude-sonnet-4-6")
    agent = create_agent(llm, tools, system_prompt=system_prompt)

    result = await agent.ainvoke(
        {"messages": [("user", "What content do we have?")]}
    )
    print(result["messages"][-1].content)


asyncio.run(main())

Every Sanity Context integration follows three steps:

• Fetch initial context via the /initial-context HTTP endpoint and inject it into your system prompt. This gives the agent a compressed schema overview so it can write accurate queries from the start — and saves a tool call on every conversation.
• Connect to MCP and get tools: Authenticate with your Sanity API read token. MultiServerMCPClient handles the MCP-to-LangChain tool conversion. Filter out the initial_context tool since you've already fetched it.
• Create the agent and call the LLM using create_agent from LangChain. The agent will make tool calls as it explores your content.

Three failures account for most first runs:

• KeyError: 'SANITY_CONTEXT_MCP_URL': The .env file is missing, or isn't in the directory you run the script from. load_dotenv() returns without error when it finds no file.
• httpx.HTTPStatusError: Client error '401 Unauthorized' raised by resp.raise_for_status(): The value in SANITY_API_READ_TOKEN isn't a valid Sanity API read token.
• Anthropic authentication failed: no API key or authorization credentials were provided.: Set ANTHROPIC_API_KEY in the .env file. ChatAnthropic constructs without a key and fails on the first call instead.

• Sanity Context patterns and best practices: Production patterns for public assistants, personalized agents, and multi-backend setups.
• Add insights to Sanity Context: Track and analyze agent conversations.
• AI shopping assistant walkthrough: A full reference implementation using Next.js and the Vercel AI SDK.