How to integrate Semrush MCP with Pydantic AI

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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', list ad copies seen for my competitor 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.

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.

Supported Tools & Triggers

Tools
Get ad copiesRetrieves 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 textsUse 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 profileRetrieves 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 backlinksFetches backlinks for a specified domain or url as a csv-formatted string, allowing customization of columns, sorting, and filtering; ensure `display limit` surpasses `display offset` when an offset is used, and note the `urlanchor` filter may have limitations for targets with extensive backlinks.
Backlinks overviewProvides a csv-formatted summary of backlinks, including authority score and link type breakdowns, for a specified and publicly accessible domain, root domain, or url.
Batch comparisonCompares backlink profiles for multiple specified targets (domains, subdomains, or urls) to analyze and compare link-building efforts.
Batch keyword overviewFetches 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 keywordFetches broad match keywords for a given phrase; `display sort` and `display filter` parameters are defined but currently not utilized by the api call.
Get categoriesRetrieves categories and their 0-1 confidence ratings for a specified domain, subdomain, or url, with results sorted by rating.
Get categories profileRetrieves 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 dataRetrieves a customizable csv report of competitors for a specified target (root domain, domain, or url) based on shared backlinks or referring domains, ensuring the target is valid and its type is correctly specified.
Get competitors in organic searchUse 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 searchRetrieves a list of a domain's competitors in paid search results from a specified regional database.
Get domain ad historyRetrieves 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 pagesFetches 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 keywordsRetrieves 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 subdomainsRetrieves 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 keywordsFetches keywords driving paid search traffic to a specified, existing domain using a supported semrush regional database.
Get PLA search keywords for a domainRetrieves product listing ad (pla) search keywords for a specified domain from a semrush regional database.
Compare domainsAnalyzes 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 dataRetrieves monthly historical backlink and referring domain data for a specified root domain, returned as a time series string with newest records first.
Get indexed pagesRetrieves 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 difficultyDetermines 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 databasesFetches a keyword overview from semrush for a specified phrase, including metrics like search volume, cpc, and competition.
Get keyword overview for one databaseFetches a keyword summary for a specified phrase from a chosen regional database.
Get keywords ads historyFetches 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 resultsRetrieves 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 resultsFetches domains ranking in google's paid search results (adwords) for a specified keyword and regional database.
Phrase questionsFetches 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 competitorsRetrieves domains competing with a specified domain in google's product listing ads (pla) from a given semrush regional database.
Get PLA copiesFetches product listing ad (pla) copies that semrush observed for a domain in google's paid search results.
Get referring domainsRetrieves a report as a text string (e.
Get referring domains by countryGenerates a csv report detailing the geographic distribution of referring domains (by country, determined via ip address) for a specified, publicly accessible target.
Referring i psFetches ip addresses that are sources of backlinks for a specified target domain, root domain, or url.
Find related keywordsCall 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 distributionFetches a report on the top-level domain (tld) distribution of referring domains for a specified target, useful for analyzing geographic or categorical backlink diversity.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

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

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.

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

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

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

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

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

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

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:

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.

How to build Semrush MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Semrush MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Semrush MCP with Claude Agent SDK

Google ADK

Google ADK

Use Semrush MCP with Google ADK

LangChain

LangChain

Use Semrush MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Semrush MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Semrush MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Semrush MCP with LlamaIndex

CrewAI

CrewAI

Use Semrush MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Semrush MCP?

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.

Can I use Tool Router MCP with Pydantic AI?

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.

Can I manage the permissions and scopes for Semrush while using Tool Router?

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.

How safe is my data with Composio Tool Router?

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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