Semrush MCP Server for Pydantic AI 8 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Semrush through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Semrush "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Semrush?"
)
print(result.data)
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.
Pydantic AI validates every Semrush tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Semrush MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 with type-safe schemas
Why Use Pydantic AI with the Semrush MCP Server
Pydantic AI provides unique advantages when paired with Semrush through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Semrush integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Semrush connection logic from agent behavior for testable, maintainable code
Semrush + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Semrush MCP Server delivers measurable value.
Type-safe data pipelines: query Semrush with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Semrush tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Semrush and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Semrush responses and write comprehensive agent tests
Semrush MCP Tools for Pydantic AI (8)
These 8 tools become available when you connect Semrush to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Semrush to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiSemrush + Pydantic AI FAQ
Common questions about integrating Semrush MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.