Semrush MCP Server for LangChain 8 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Semrush through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"semrush": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Semrush, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Semrush MCP Server
Equip your conversational workflow with the raw data power of Semrush, the industry standard for Digital Marketing visibility. Through this server, your AI can pull immense amounts of SERP forensics directly into the context window. Stop switching tabs to look up keyword difficulty—just command your agent to fetch it seamlessly.
LangChain's ecosystem of 500+ components combines seamlessly with Semrush through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Deep Domain Forensics (Competitors) — Query
domain_overviewordomain_vs_domainto tell your agent to digest the exact organic search volume differences between you and a rival - Keyword Strategy Building — Hand a seed topic to the LLM and invoke
related_keywords. The AI will compile comprehensive editorial briefs loaded with actual search volumes and CPCs - Backlink Auditing — Track the inbound link profile (
get_backlinks) of external domains to gauge authority natively within chat sessions - Technical SEO Interrogation — Quickly bring your technical
site_auditscore to the AI, asking it to explain what the flagged errors mean and draft instructions to fix missing metadata
The Semrush MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Semrush to LangChain via MCP
Follow these steps to integrate the Semrush MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 8 tools from Semrush via MCP
Why Use LangChain with the Semrush MCP Server
LangChain provides unique advantages when paired with Semrush through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Semrush MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Semrush queries for multi-turn workflows
Semrush + LangChain Use Cases
Practical scenarios where LangChain combined with the Semrush MCP Server delivers measurable value.
RAG with live data: combine Semrush tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Semrush, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Semrush tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Semrush tool call, measure latency, and optimize your agent's performance
Semrush MCP Tools for LangChain (8)
These 8 tools become available when you connect Semrush to LangChain via MCP:
domain_overview
Specify the database (e.g., "us", "uk") if targeting a specific region. Get domain SEO overview: rank, organic traffic, paid traffic
domain_vs_domain
Compare two domains SEO side by side
get_backlinks
Get backlink overview for a domain
keyword_overview
Get keyword metrics: volume, CPC, competition, SERP features
organic_keywords
Useful for competitor analysis or performance tracking. Get domain organic keyword positions
related_keywords
Ideal for content planning and SEO expansion. Get related keywords with volume and difficulty
site_audit
Requires a valid Semgrep project ID. Get site audit quality overview for a project
traffic_analytics
Get traffic analytics: visits, bounce rate, pages/visit
Example Prompts for Semrush in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Semrush immediately.
"Pull the foundational organic ranking and paid traffic overview for the domain 'airbnb.com'. Target the US database."
"Find 10 related keywords for the term 'buy mechanical keyboard' including their respective difficulties and search volumes."
"Compare the overarching inbound domain performance between 'coca-cola.com' and 'pepsi.com'."
Troubleshooting Semrush MCP Server with LangChain
Common issues when connecting Semrush to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersSemrush + LangChain FAQ
Common questions about integrating Semrush MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Semrush with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Semrush to LangChain
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.