How to Use the Cloudflare MCP in LangChain

Q: How do I run SQL queries on Cloudflare D1 databases using LangChain?

Use the queryd1 tool inside your LangChain agent's toolset via the MCP Server. The agent finds the correct database using listd1databases and executes SELECT or UPDATE statements directly.

Q: Can a LangChain agent manage secret keys on Cloudflare?

Yes, the agent calls createsecret to write encrypted variables to your Workers. It audits existing keys with listsecrets and removes old ones using deletesecret.

Q: How does LangChain handle gradual rollouts on Cloudflare?

Your LangChain chain uses the MCP Server's createdeployment tool to set percentage-based traffic splits. It checks the live status by calling listdeployments in subsequent steps.

Q: How do I route traffic to new Workers?

The agent maps Workers to specific domains with createworkerroute. You can inspect your active routing rules at any time using listworkerroutes.

Build multi-step LangChain pipelines that deploy Workers, query D1 databases, and manage your Cloudflare edge infrastructure.

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Connect Cloudflare MCP to LangChain

Create your Vinkius account to connect Cloudflare to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cloudflare with LangChain

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Automate Cloudflare deployments in LangChain chains

`create_deployment` lets your LangChain agent roll out specific Worker versions using immediate or gradual, percentage-based traffic strategies. Your agent inspects the history with `list_deployments` to decide if a rollout needs to be paused or modified based on previous steps in your execution chain. Connecting this MCP Server to your LangChain agent allows it to chain these actions with `list_worker_versions` to verify exactly which code snapshot is active before pushing updates. This setup removes the manual steps of checking your terminal or logging into a dashboard during complex CI pipelines.

Query D1 and clear cache dynamically with LangChain

`query_d1` runs SQL commands against your D1 databases directly from a LangChain reasoning loop using this MCP Server. The agent retrieves database IDs using `list_d1_databases` and executes queries to update application state or run migrations. After updating database records, your LangChain chain can automatically trigger `purge_cache` to clear stale edge assets. This ensures your global users instantly see the fresh data written by the SQL query.

Manage Worker secrets and logs in LangChain

`create_tail_session` starts a live log streaming session to capture exceptions and console output from your active Workers. Your LangChain agent monitors these streams and automatically cleans up when finished using `delete_tail_session`. When an API key changes, the agent uses `create_secret` to inject encrypted environment variables into your runtime. It verifies active configurations via `list_secrets` without ever exposing the sensitive values in the LangSmith trace logs.

Setup guide

Set up Cloudflare MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Cloudflare tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "cloudflare-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Cloudflare transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Cloudflare MCP in LangChain

Use the `query_d1` tool inside your LangChain agent's toolset via the MCP Server. The agent finds the correct database using `list_d1_databases` and executes SELECT or UPDATE statements directly.

Yes, the agent calls `create_secret` to write encrypted variables to your Workers. It audits existing keys with `list_secrets` and removes old ones using `delete_secret`.

Your LangChain chain uses the MCP Server's `create_deployment` tool to set percentage-based traffic splits. It checks the live status by calling `list_deployments` in subsequent steps.

The agent maps Workers to specific domains with `create_worker_route`. You can inspect your active routing rules at any time using `list_worker_routes`.

The Cloudflare MCP Server runs in a zero-trust sandbox where your API tokens never touch LangChain's servers. Secret values written via `create_secret` are encrypted immediately at rest and injected directly into the Worker runtime.

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