Meta tags and on-page content Optimize meta tags and content with Gemini CLI
Optimizing meta tags and on-page content with Gemini CLI means asking Google's terminal agent to have StoreSEO preview generated meta for a product, reading the draft in the session, and confirming the apply step tool by tool.
Last updated: October 2026
Set up Gemini CLI for StoreSEO
Skip this if Gemini CLI is already connected. The full guide, with the alternatives, is on the Gemini CLI page.
- 1
Add StoreSEO to Gemini CLI
Run this in any terminal. The user scope writes the server to ~/.gemini/settings.json so every project sees it. Without --scope user the command writes to the current project's .gemini/settings.json.
Terminalgemini mcp add --scope user --transport http storeseo https://mcp.storeseo.com/mcp
- 2
Sign in to your store
Start gemini. The first time it calls StoreSEO it opens your browser: pick your Shopify store and approve access. If the window does not open, type /mcp auth storeseo inside Gemini CLI. Then gemini mcp list should show storeseo as connected.
Prompt/mcp auth storeseo
How does this work in Gemini CLI?
You start gemini in a terminal, in your theme folder or anywhere else, with the storeseo server added and signed in. You ask for meta to be improved for a named product or for the lowest-scoring few. Gemini CLI calls the StoreSEO tools, which it lists to the model as mcp_storeseo_storeseo_start and so on, beginning with storeseo_start, then storeseo_get_resource_seo to read the current fields and failing criteria, then storeseo_preview_resource_optimization. StoreSEO generates the meta title, description, tags, FAQs, AI snippet and any handle change on its servers and returns a projected score; Gemini CLI reads that back to you.
Gemini CLI shows a confirmation for MCP tool calls unless the server is marked trusted, and for this job that is useful in two places. On the preview you confirm that you accept the AI Content Optimizer credit cost, and on the apply you see the arguments naming which axes will be saved. Before the apply, the agent should show the old and new URL if the handle changes and tell you a redirect will be created; answer in the session with the parts to apply, for example the meta but not the FAQs. After the apply, the response gives the recomputed score and what still needs a manual fix.
Everything stays in one terminal session, so you can hand Gemini CLI follow-up work in the same breath: write the before and after scores to a file, or search the project for links to the old handle. Drafts last an hour, and a fresh preview spends credit again, so apply or abandon a draft in the same sitting. If you are on a headless machine, sign in with an access token header instead of the browser flow.
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You: Preview better meta for the merino-beanie product. I accept the preview credit.
- storeseo_start read-only
- storeseo_get_resource_seo read-only
- storeseo_preview_resource_optimization preview
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Gemini CLI: The draft lifts the score from 38 to 72 with new meta, tags and an AI snippet. The handle would change from /beanie-2 to /merino-beanie and the old URL would redirect. Apply all of it?
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You: Yes, but keep the current handle.
- storeseo_apply_resource_optimization needs approval
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Gemini CLI: Applied the meta, tags and AI snippet with the handle unchanged. The score is now 69.
What to ask Gemini CLI
Use StoreSEO to preview optimized meta for the product merino-beanie. Show me the draft, the score change and any URL handle change, and wait for my yes.
Preview a new meta title and description for my About page, tell me what the preview will spend, and do not apply it yet.
Apply the title and description from that preview, skip the handle, and save the before and after scores to meta-changes.md.
- Optimize the meta title and description for my five lowest-scoring products. Show me the previews before you apply anything.
- Preview a new meta title, description and FAQ for the collection Summer Dresses, and keep the current URL handle.
- Apply the meta from that preview but skip the FAQ.
- Which of my pages have no meta description? Draft one for each and wait for my approval.
- What does StoreSEO optimize automatically for new products right now?
- Turn on automatic meta and alt text for new products only. Tell me what that will spend first.
What StoreSEO does: meta tags and on-page content
StoreSEO generates the content server-side: meta title and description, tags, a keyword-led URL handle, FAQs and an AI snippet. The agent shows you the draft and the projected score change before anything is saved, then applies all of it or only the parts you approve.
Requires: Generating a preview spends AI Content Optimizer credit; applying a preview is free.
- 1
Read the current state
The agent reads the resource's SEO record so it knows which fields exist, which fail and what the preview will add.
- 2
Generate a preview
StoreSEO writes the meta title, description, tags, URL handle, FAQs and AI snippet and projects the new SEO score, plus the GEO score for products. Nothing is saved yet. Preview spends credit whether or not you apply it.
- 3
Show you the draft
The agent presents the generated text and the score change. When the handle changes it shows the old and new URL and tells you a redirect will be created.
- 4
Apply what you approve
After your yes it applies everything, or only the parts you chose, for example the meta but not the FAQs. Skipped fields stay available from the same draft for an hour.
- 5
Report the new score
The apply call returns the recomputed score and the remaining checklist, so the agent tells you what changed and offers the next most useful fix.
Meta tags and on-page content reference
| Generated fields | Meta title, meta description, tags, URL handle, FAQs, AI snippet |
|---|---|
| Resource types | Products, collections, pages and articles (collections have no meta tags or tags axis in auto settings) |
| Who writes the copy | StoreSEO, server-side. The agent never authors meta, FAQs or handles itself |
| Cost | Preview spends AI Content Optimizer credit; apply is free; a new preview spends again |
| Draft lifetime | One hour after the preview |
| URL handle | Applying a new handle changes the live URL and creates a redirect from the old one; a handle already in use is dropped automatically |
| Partial apply | Apply a subset of axes, or skip individual fields such as the handle |
| Auto optimization | Optional settings that generate content for newly synced resources; only future syncs, nothing is backfilled |
| Auto settings plan | Configurable on any plan; switching an auto toggle on needs a paid plan and no active trial |
Rules Gemini CLI follows
- The agent previews first and applies only after you confirm the previewed content.
- When a handle changes, you see the old URL and the new URL before the apply call, because applying creates a redirect.
- A request like optimize my worst five products authorizes the previews; the agent should remind you that previews spend credit even if you do not apply them.
- Turning on auto optimization spends credit on every future sync, so the agent proposes the exact sections and waits for your yes.
- Product title, focus keyword and description body are not written by this job; the description has its own tool in the AI search job.
In Gemini CLI
Do not mark the storeseo server as trusted for this job. The trust setting skips Gemini CLI's tool confirmations, which would remove the client-side stop on both the credit-spending preview and the apply, and then the confirmation StoreSEO asks for in the session is the only check, an instruction the model follows and not a lock. If you need fewer prompts, use includeTools to expose only the read tools plus the preview and apply tools for this task rather than trusting everything. Also note Google's notice that Gemini CLI was replaced by Antigravity CLI for unpaid tier and Google One users, so confirm your account can still use it.
What runs without asking. Reads such as SEO scores, store listings, settings and Google reports run straight away. Generating a preview spends AI credit but writes nothing, and StoreSEO tells the agent to say what a generation will cost before it runs one. Applying content, optimizing images, changing settings, publishing llms.txt or agents.md, writing a product description and deleting anything wait for your yes in the conversation. That yes is an instruction the agent follows, not a lock on the server, so your client's own tool-approval setting is the hard stop.
Questions
Should I set trust to true for the storeseo server in Gemini CLI?
Not for meta optimization. Trust skips every tool confirmation, including the one on the apply step. Leave it off so Gemini CLI prompts, or use includeTools to limit which StoreSEO tools the model can see.
Can Gemini CLI run this on a server without a browser?
Yes, with an access token. The browser sign-in needs a browser on the same machine, so on a headless host add the server with an Authorization Bearer header using a token from Settings, AI Toolkit, then run the same preview and apply flow.
Does previewing an optimization change my product?
No. A preview generates the content and projects the score without saving anything. It does spend AI Content Optimizer credit, and applying the draft afterwards is free.
What happens to the old URL when the handle changes?
Applying a new URL handle updates the live URL and StoreSEO creates a redirect from the old one, so existing links keep working. The agent should show you both URLs before it applies the change.
Can I apply only part of a preview?
Yes. The apply step takes the axes you approved, and individual content fields such as the URL handle can be skipped. Skipped parts stay available from the same draft for one hour.
Does auto optimization fix my existing products?
No. Auto optimization only acts on resources synced or updated after you turn it on. Existing products are fixed with the preview and apply steps in this job.
Will StoreSEO change my product title?
No. This job writes the SEO meta title, not the storefront product title. The product title is edited in Shopify admin.
Keep going
Meta tags and on-page content with other agents
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