Semrush MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Semrush as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Semrush. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Semrush?"
)
print(response)
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.
LlamaIndex agents combine Semrush tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Semrush MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Semrush
Why Use LlamaIndex with the Semrush MCP Server
LlamaIndex provides unique advantages when paired with Semrush through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Semrush tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Semrush tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Semrush, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Semrush tools were called, what data was returned, and how it influenced the final answer
Semrush + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Semrush MCP Server delivers measurable value.
Hybrid search: combine Semrush real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Semrush to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Semrush for fresh data
Analytical workflows: chain Semrush queries with LlamaIndex's data connectors to build multi-source analytical reports
Semrush MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Semrush to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Semrush to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSemrush + LlamaIndex FAQ
Common questions about integrating Semrush MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.