Optimize meta tags and content with GitHub Copilot
Optimizing meta tags and on-page content with GitHub Copilot means using Copilot Chat in Agent mode inside VS Code to have StoreSEO preview generated meta for a product, reading the draft beside your code, and approving the apply step in the editor.
Last updated: October 2026
Set up GitHub Copilot for StoreSEO
Skip this if GitHub Copilot is already connected. The full guide, with the alternatives, is on the GitHub Copilot page.
- 1
Add StoreSEO to .vscode/mcp.json
Create or open .vscode/mcp.json in your workspace and paste this. VS Code uses a top-level servers key, not mcpServers, and a remote server needs type http.
JSON{ "servers": { "storeseo": { "type": "http", "url": "https://mcp.storeseo.com/mcp" } } } - 2
Start the server and sign in
Run MCP: List Servers from the Command Palette, pick storeseo and start it. If it asks you to sign in, pick your Shopify store in the browser and approve access. Then open Copilot Chat in Agent mode, check the tools icon lists StoreSEO and ask this.
PromptUse StoreSEO to show me my store's SEO score breakdown.
How does this work in GitHub Copilot?
You have the theme workspace open in VS Code, the storeseo server listed in .vscode/mcp.json and started from MCP: List Servers, and Copilot Chat switched to Agent mode. You ask for the meta of a product to be improved. Copilot calls storeseo_start, reads the resource with storeseo_get_resource_seo and then calls storeseo_preview_resource_optimization. StoreSEO generates the meta title, description, tags, FAQs, AI snippet and any handle change on its servers and projects the new score. Copilot reads the draft back in the chat, so the text you review is StoreSEO's, not the model's.
VS Code adds its own confirmation to every tool call. In Agent mode it shows a prompt with the tool name and its input, and you can allow it once, for the session, for the workspace or always. That prompt is the hard stop, and the yes you give in the chat is the instruction StoreSEO tells the agent to wait for. For this job use the prompt differently on each tool: allowing the preview once is a way to accept the AI Content Optimizer credit cost, and the apply prompt deserves a read of the arguments, which list the axes being saved and any fields skipped, such as the handle.
Working in the editor means the theme is right there. If the preview proposes a new handle, ask Copilot to search the workspace for links to the old path, or to check whether the product template renders a hard-coded title tag that would override your new meta. A fresh preview spends credit again and the draft lasts an hour, so settle one product before starting the next. Business and Enterprise users may find the server blocked until an admin has enabled MCP servers in Copilot policy.
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You: Preview better meta for the merino-beanie product. A preview spends credit and that is fine.
- storeseo_start read-only
- storeseo_get_resource_seo read-only
- storeseo_preview_resource_optimization preview
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GitHub Copilot: The draft raises the score from 35 to 73 with new meta, tags and two FAQs. The handle would change from /beanie-2 to /merino-beanie with a redirect from the old URL. Apply everything?
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You: Apply the meta and tags only.
- storeseo_apply_resource_optimization needs approval
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GitHub Copilot: Applied the meta and tags. The FAQs and handle stay in the draft for about an hour. The score is now 66.
What to ask GitHub Copilot
Use StoreSEO to preview optimized meta for the product merino-beanie. Show me the draft, the projected score and any URL handle change, and wait for my approval.
Preview a new meta title, description and FAQ for my Summer Dresses collection. Then search this workspace for links to its current handle.
Apply the meta from that preview, skip the FAQ and the handle, and tell me the new score.
- 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 GitHub Copilot 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 GitHub Copilot
VS Code lets you approve a tool for the session, the workspace or always, and Allow always on the apply tool removes the editor's hard stop, leaving only the confirmation StoreSEO asks for in the chat, which is an instruction to the agent and not a lock. Keep storeseo_apply_resource_optimization on a per-call prompt. Also remember the file-format trap: the workspace file uses the servers key, so a snippet copied from a Cursor or Claude guide will not load, and then Agent mode will not list the preview or apply tools at all.
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
Why does Copilot not list the StoreSEO preview tool in Agent mode?
Check that .vscode/mcp.json uses the servers key and type http, that the server is started in MCP: List Servers, and that the tools icon in the chat includes StoreSEO. On Business and Enterprise plans an admin must also enable MCP servers in Copilot policy.
Does Copilot apply meta changes to my code?
No. The apply tool saves the SEO fields to your Shopify store through StoreSEO. Copilot edits files in your workspace only when you separately ask it to, for example to remove a hard-coded title tag in the theme.
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
More GitHub Copilot guides