Research keywords for your job board with AI

First, connect Cavuno to your AI client. Cavuno MCP lets the agent understand the job board it is researching: its domain, existing blog posts, job categories, skills, companies, and other taxonomies. The agent can then use its browser and any SEO tools you have connected to research how that audience searches.

Keyword research should produce decisions, not a large spreadsheet of phrases. The useful output is a set of topic clusters with clear search intent, evidence, existing coverage, and a recommended next action.

Try it

Ask:

Use Cavuno to understand my job board, audience, existing blog coverage, and the kinds of roles and companies it serves. Then research the searches that matter to that audience. Group close variants into topic clusters and show the search intent, likely page type, journey stage, evidence source, existing Cavuno coverage, and recommended next action for each cluster. Do not assume every keyword should become a blog post.

If you have SEO tools available, tell the agent to use them:

Repeat the research using the Google Search Console, Ahrefs, and Semrush data available to you. Keep first-party Search Console queries separate from third-party estimates, cite which tool supports each recommendation, and flag any metric you could not verify.

If those tools are unavailable, the browser can still provide useful qualitative research:

Use the live search results, related searches, audience questions, and the pages currently ranking to expand and validate the clusters. Do not invent search volume or keyword difficulty.

What the agent will do

1. Use Cavuno MCP to inspect the board’s domain, existing posts, taxonomies, and representative jobs so it can describe the actual niche instead of asking you for information it can discover.
2. Build a compact, paginated post inventory from titles, slugs, excerpts, tags, and dates. It fetches full content in batches only for posts likely to overlap a topic cluster. It also recognises job, company, category, skill, and location pages that Cavuno already creates so it does not recommend duplicating those surfaces as articles.
3. Collect keyword evidence from the tools available to the client. Google Search Console can reveal first-party queries already associated with the site; Ahrefs and Semrush can add estimated demand, difficulty, and competitor coverage; browser research can verify the live intent and page type.
4. Group close variants by shared search intent rather than treating every wording variation as a separate article. It separates queries that deserve different pages because the reader expects a different answer or format.
5. Classify the dominant page type for each cluster. If searchers want a job listing, directory, calculator, template, product, or discussion, it does not force that intent into a generic advice post.
6. Prioritise SEO viability and business value separately. A large keyword can be a poor fit, while a smaller problem-led query can lead naturally from useful advice to searching jobs, subscribing, or posting a role.
7. Check the recommendations against Cavuno again and return a verified research set with the evidence, uncertainty, and next action: investigate a content gap, refresh an existing post, create a new article, improve a non-blog page, or skip the topic.

Cavuno MCP cannot access Google Search Console, Ahrefs, Semrush, or live search results. Those sources come from the AI client outside Cavuno; MCP supplies the job board context needed to interpret them accurately.