Matthew Widdop
Matty is a Digital SEO Executive at Intelligency, helping our clients to improve their digital performance. Matty is currently studying for his Level 3 Multi-Channel Marketer apprenticeship and has completed a Sport Journalism Degree at the University of Huddersfield.
Keyword research is one of the most important tasks for a successful, co-ordinated SEO campaign. By understanding how your users are searching, you can specifically tailor your content around the keywords they are using to find your products and services. This helps to boost your rankings on search engines, such as Google Search, allowing your services to gain extra visibility.
However, keyword research can often be a long, detailed and painstakingly resource heavy task. Your business may have hundreds or thousands of keywords relevant to their industry, with various degrees of importance, that need identifying and collating. This is where AI comes in. AI can be used to improve keyword research workflow, allowing SEOs to quickly identify keyword opportunities to improve their content.
How it works
Most SEOs will have a preferred keyword research tool that they use in-house. Some popular examples include keyword research tools from SemRush, Ahrefs and SERanking. Here at Intelligency, we use SERanking for our keyword research. This involves manually searching for keywords, SERanking will then show you the keyword data for said keyword along with other relevant keywords that are related.
Now with ChatGPT you can connect platforms such as SERanking directly to AI chatbots, such as ChatGPT and Claude and use a prompt using AI to pull through all relevant keywords into one keyword research document, massively improving time efficiency.
You do this by using a Model Context Protocol (MCP) . This piece of software allows different AI models to talk to different software, such as SEO tools, so they understand how to extract the information you require.
Briefing to AI Agents
If you are going to use AI to help with your keyword research workflow, getting the brief correct is one of the most important steps, if AI doesn’t understand the brief precisely then it won’t pull through the correct data.
We will use an example brief to Claude to show how to successfully explain to AI how to create a precise keyword research document. For this example, we will be highlighting a brief for a dental client who wants to expand their keyword research for their various dental practices around the UK.
Here is a list of dental locations from my client X. I want you to connect to SERanking and use their keyword research tool to gather relevant keyword information based around each location. Each clinic should have keywords related to its location for example:
Local
- dentist Bristol
- dentist in Bristol
- Bristol dentist
- dentist (village or town name)
- dentist (region)
Treatment + local
- veneers Bristol
- teeth whitening Bristol
- best veneers Bristol
- dentist veneers Bristol
NHS + local
- nhs dentist Bristol
- nhs appointment Bristol
General + local
- cosmetic dentistry Bristol
- emergency dentist Bristol
- dental practice Bristol
Don’t include keywords with no search volume and include other treatments and keywords outside of those listed if relevant, this is only a general idea of the keyword structure.
By using this prompt I have given Claude a comprehensive set of instructions on how to structure the keyword data it sends me. Claude sent me back other 500 relevant keywords for my sector including specific keyword data such as cost-per-click, search volume and SERP competition.
This method of using an MCP can shave hours of time off your workload, allowing you to focus on other tasks or fine tune your content using your new AI enhanced keyword data.
