How to integrate Semrush MCP with Pydantic AI

This guide walks you through connecting Semrush to Pydantic AI using the Composio tool router. By the end, you'll have a working Semrush agent that can show top anchor texts for example.com, compare backlink profiles for three domains, get keyword overview for 'organic coffee' through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Semrush account through Composio's Semrush MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Introduction

This guide walks you through connecting Semrush to Pydantic AI using the Composio tool router. By the end, you'll have a working Semrush agent that can show top anchor texts for example.com, compare backlink profiles for three domains, get keyword overview for 'organic coffee' through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Semrush account through Composio's Semrush MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Semrush with

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Semrush
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Semrush workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the Semrush MCP server, and what's possible with it?

The Semrush MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Semrush account. It provides structured and secure access to your SEO, keyword, and advertising analytics, so your agent can perform actions like keyword research, competitor analysis, backlink audits, and ad copy retrieval automatically on your behalf.

  • Comprehensive keyword research and reporting: Let your agent fetch broad match keywords, generate batch keyword overviews, and analyze key SEO metrics like search volume and difficulty in real time.
  • Competitor and backlink analysis: Ask your agent to pull backlink profiles, perform batch comparisons of domains, and summarize backlink authority and link types for competitive intelligence.
  • Ad campaign and copy insights: Have the agent retrieve unique Google Ads copies for any domain, helping you benchmark and optimize your own ad strategies based on real competitor data.
  • Content and category profiling: Enable your agent to analyze and categorize domains or URLs, surfacing topic strengths and audience focus areas for smarter content planning.
  • Anchor text and authority monitoring: Direct your agent to report on anchor text distributions and authority score profiles, giving you actionable insights for improving link-building efforts.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Semrush
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Semrush
  • MCPServerStreamableHTTP connects to the Semrush MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Semrush
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["semrush"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Semrush tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
semrush_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[semrush_mcp],
    instructions=(
        "You are a Semrush assistant. Use Semrush tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Semrush endpoint
  • The agent uses GPT-5 to interpret user commands and perform Semrush operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Semrush.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Semrush API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Semrush and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Semrush
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["semrush"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    semrush_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[semrush_mcp],
        instructions=(
            "You are a Semrush assistant. Use Semrush tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Semrush.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Semrush through Composio's Tool Router. With this setup, your agent can perform real Semrush actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Semrush for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Semrush action and event your agent gets out of the box.

Check Semrush account units balance

Tool to fetch the remaining Semrush Standard API units for the authenticated account.

Get ad copies

Retrieves unique ad copies Semrush has observed for a specified domain from a regional database, detailing ads seen in Google's paid search results.

Get anchor texts

Use this action to get a CSV report of anchor texts for backlinks pointing to a specified, publicly accessible domain, root domain, or URL.

Get authority score profile

Retrieves the Authority Score (AS) profile for a specified target, showing the count of referring domains that link to the target for each AS value from 0 to 100.

Get backlinks

Fetches backlinks for a specified domain or URL as a semicolon-delimited CSV string (parse with `sep=';'`); allows customization of columns, sorting, and filtering.

Backlinks overview

Provides a semicolon-delimited (sep=';') CSV summary of backlinks, including Authority Score and link type breakdowns, for a specified and publicly accessible domain, root domain, or URL.

Batch comparison

Compares backlink profiles for multiple specified targets (domains, subdomains, or URLs) to analyze and compare link-building efforts.

Batch keyword overview

Fetches a keyword overview report from a Semrush regional database for up to 100 keywords, providing metrics like search volume, CPC, and keyword difficulty.

Broad match keyword

Fetches broad match keywords for a given phrase.

Get categories

Retrieves categories and their 0-1 confidence ratings for a specified domain, subdomain, or URL, with results sorted by rating.

Get categories profile

Retrieves a profile of content categories from referring domains for a specified target, analyzing its first 10,000 referring domains and sorting results by domain count.

Get competitor data

Retrieves a CSV-formatted report of competitors for a specified target (root domain, domain, or URL) based on shared backlinks or referring domains.

Get competitors in organic search

Use to get a domain's organic search competitors from Semrush as a semicolon-separated string; `display_date` requires 'YYYYMM15' format if used.

Get competitors in paid search

Retrieves a list of a domain's competitors in paid search results from a specified regional database.

Get domain ad history

Retrieves a domain's 12-month advertising history from Semrush (keywords bid on, ad positions, ad copy) for PPC strategy and competitor analysis; most effective when the domain has ad history in the selected database.

Get domain organic pages

Fetches a report on a domain's unique organic pages ranking in Google's top 100 search results, with options for specifying database, date, columns, sorting, and filtering.

Get domain organic search keywords

Retrieves organic search keywords for a domain from a specified Semrush regional database; `display_positions` must be set if `display_daily=1` for daily updates.

Get domain organic subdomains

Retrieves a report on subdomains of a given domain that rank in Google's top 100 organic search results for a specified regional database.

Get domain paid search keywords

Fetches keywords driving paid search traffic to a specified, existing domain using a supported Semrush regional database.

Get PLA search keywords for a domain

Retrieves Product Listing Ad (PLA) search keywords for a specified domain from a Semrush regional database.

Compare domains

Analyzes keyword rankings by comparing up to five domains to find common, unique, or gap keywords, using specified organic/paid types and comparison logic in the `domains` string.

Get historical data

Retrieves monthly historical backlink and referring domain data for a specified root domain, returned as a time series string with newest records first.

Get indexed pages

Retrieves a list of indexed pages from Semrush for a specified `target` (root domain, domain, or URL) and `target_type`, ensuring `target` is publicly accessible, Semrush-analyzable, and correctly matches `target_type`.

Get keyword difficulty

Determines the Keyword Difficulty (KD) score (0-100, higher means greater difficulty) for a given phrase in a specific Semrush regional database to assess its SEO competitiveness.

Keyword overview all databases

Fetches a keyword overview from Semrush for a specified phrase, including metrics like search volume, CPC, and competition.

Get keyword overview for one database

Fetches a keyword summary for a specified phrase from a chosen regional database.

Get keywords ads history

Fetches a historical report (last 12 months) of domains advertising on a specified keyword in Google Ads, optionally for a specific month ('YYYYMM15') or the most recent period, returning raw CSV-like data.

Get organic results

Retrieves up to 100,000 domains and URLs from Google's top 100 organic search results for a keyword and region, returning a raw string; use `display_date` in 'YYYYMM15' format (day must be '15') for historical data.

Get paid search results

Fetches domains ranking in Google's paid search results (AdWords) for a specified keyword and regional database.

Phrase questions

Fetches question-format keywords semantically related to a given query phrase for a specified regional database, aiding in understanding user search intent and discovering content ideas.

Get PLA competitors

Retrieves domains competing with a specified domain in Google's Product Listing Ads (PLA) from a given Semrush regional database.

Get PLA copies

Fetches Product Listing Ad (PLA) copies that Semrush observed for a domain in Google's paid search results.

Get referring domains

Retrieves a semicolon-delimited text report listing domains that link to a target, with options to filter by type (not value).

Get referring domains by country

Generates a CSV report detailing the geographic distribution of referring domains (by country, determined via IP address) for a specified, publicly accessible target.

Referring i ps

Fetches IP addresses that are sources of backlinks for a specified target domain, root domain, or URL.

Find related keywords

Call this to find related keywords (including synonyms and variations) for a target phrase in a specific regional database; `display_date` (if used for historical data) must be 'YYYYMM15' for a past month.

Get TLD distribution

Fetches a report on the Top-Level Domain (TLD) distribution of referring domains for a specified target, useful for analyzing geographic or categorical backlink diversity.

FRAMEWORKS

How to build Semrush MCP Agent with another framework

ChatGPT logo

ChatGPT

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OpenAI Agents SDK logo

OpenAI Agents SDK

Use Semrush MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Semrush MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Semrush MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Semrush MCP with Claude Cowork

Codex logo

Codex

Use Semrush MCP with Codex

Cursor logo

Cursor

Use Semrush MCP with Cursor

OpenClaw logo

OpenClaw

Use Semrush MCP with OpenClaw

Hermes logo

Hermes

Use Semrush MCP with Hermes

CLI logo

CLI

Use Semrush MCP with CLI

Google ADK logo

Google ADK

Use Semrush MCP with Google ADK

LangChain logo

LangChain

Use Semrush MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Semrush MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Semrush MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Semrush MCP with LlamaIndex

CrewAI logo

CrewAI

Use Semrush MCP with CrewAI

OpenCode logo

OpenCode

Use Semrush MCP with OpenCode

VS Code logo

VS Code

Use Semrush MCP with VS Code

FAQ

Frequently asked questions

With a standalone Semrush MCP server, the agents and LLMs can only access a fixed set of Semrush tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Semrush and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Pydantic AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Semrush tools.

Yes, absolutely. You can configure which Semrush scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Semrush data and credentials are handled as safely as possible.

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A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

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Co-founder, Opennote

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As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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