Ubersuggest

Ubersuggest is an MCP server that integrates Neil Patel's Ubersuggest platform directly with Model Context Protocol-compatible AI assistants. It connects large language models to Ubersuggest SEO capabilities, enabling marketing specialists, SEO analysts, content creators, and growth teams to execute search optimization workflows within conversational interfaces. By serving as an intermediary between client applications and Ubersuggest, this server allows users to inspect keyword metrics, analyze domain rankings, evaluate competitor search performance, and review site optimization factors without manually navigating external web dashboards. Integrating search intelligence directly into AI workflows helps users streamline technical SEO audits, formulate data-informed content strategies, investigate organic search visibility trends, and uncover link-building or competitive opportunities. Marketers and developers using MCP-compliant tools can use this integration to automate repetitive research tasks, query optimization metrics in real time, and ground generative content creation in accurate search engine marketing datasets.

Category: CRM, ERP & E-commerce

Tags: analytics, marketing, seo, ubersuggest

Visit Ubersuggest

How to install and configure Ubersuggest

1. Visit the project repository at https://github.com/madebyaris/mcp-ubersuggest to review prerequisites and build requirements. 2. Clone the repository to your local machine or install the server according to the project documentation. 3. Configure any required environment variables or authentication tokens specified in the repository. 4. Add the server configuration to your MCP client configuration file (such as Claude Desktop or Cursor). 5. Restart your MCP client to verify the connection and begin issuing SEO analysis requests.

What you can do with Ubersuggest

• Retrieve target keyword search volumes, difficulty scores, and competitive metrics to guide content production planning. - Conduct domain-level SEO audits to discover crawling issues, indexing factors, and site performance optimization areas. - Analyze competitor search traffic, top-ranking URLs, and keyword gaps directly from an MCP conversational client. - Evaluate backlink profiles and referring domains to identify opportunities for off-page search engine optimization campaigns. - Assist copywriters in generating title tags, meta descriptions, and header outlines tailored to target keyword parameters.

Key facts

• https://github.com/madebyaris/mcp-ubersuggest
• CRM, ERP & E-commerce, Web Search & Research
• analytics, marketing, seo, ubersuggest

Part of MCP Servers

What is the Ubersuggest MCP server?

The Ubersuggest MCP server is a Model Context Protocol tool developed to link Neil Patel's Ubersuggest SEO platform to AI assistants. It provides language models with the ability to query search marketing data, review keyword performance metrics, and perform domain analysis directly in chat workflows.

How do I install the Ubersuggest MCP server?

To install the server, check the official documentation on GitHub at https://github.com/madebyaris/mcp-ubersuggest for detailed installation instructions, dependencies, and configuration steps required to connect the server to your preferred MCP client.

Which MCP clients work with Ubersuggest?

This server works with any standard Model Context Protocol client. Supported applications include Claude Desktop, Cursor, and custom agentic frameworks configured to run local or remote MCP servers.

What can Ubersuggest MCP do?

It enables AI models to perform automated search engine optimization research, including fetching keyword data, examining domain search rankings, auditing website performance, and exploring competitive intelligence without manually leaving your workspace.