Write copy in real customer language
Mine six months of mentions for the exact words customers use to describe you, then rewrite your page copy from that vocabulary — with receipts.
Set it up
1. Connect the Octolens MCP — add the server URL in your client and sign in. OAuth, no API key. Per-client instructions · docs
https://app.octolens.com/api/mcp/v22. Hand your agent the playbook — hit Copy below (it grabs the instruction plus the full playbook), fill in the placeholders, and paste it into your agent. Pasting the playbook instead of describing the idea makes the agent run this exact workflow rather than improvise its own.
Run the playbook below once. Brand: "{BRAND_NAME}". Page to rewrite: {PAGE_URL}.What it does
- ✓Reads the page you want to rewrite and notes its current claims
- ✓Pulls six months of high-relevance mentions, minus your own company's posts
- ✓Extracts the nouns, verbs, and recurring phrases customers actually use — verbatim
- ✓Shows where your page's language diverges from your customers' language
- ✓Rewrites headlines, subheadlines, and value props using only customer vocabulary
- ✓Backs every line with the real quotes it was built from
Delivers: Headline, subheadline, and value-prop options written in your customers' own words — each traceable to real quotes with source links.
Inputs
| Placeholder | Required | Description | Example |
|---|---|---|---|
| {BRAND_NAME} | Required | Your brand as tracked in Octolens | Acme |
| {PAGE_URL} | Required | The page whose copy you want rewritten | https://acme.com |
How your agent runs it
- 1
Read the current page — The agent fetches the page and notes the headline, subheadline, and claims.
web_fetch - 2
Load brand context — Grabs your workspace profile and confirms the brand keyword.
get_workspacelist_keywords - 3
Pull real mentions — Fetches up to 200 high-relevance mentions from six months, excluding your own company's posts.
list_mentions - 4
Extract the vocabulary — Collects verbatim nouns, verbs, solved problems, and phrases that recur across 3+ independent mentions, clustered into themes.
- 5
Find the divergence — Maps where the page's marketing language and the customers' actual language don't match.
- 6
Rewrite with receipts — Produces headline, subheadline, and bullet options built only from customer vocabulary — each annotated with its evidence quotes.
Tools used
get_workspaceOctolens MCPLoad the company profile used to exclude own-company postslist_keywordsOctolens MCPConfirm the brand keyword and get its IDlist_mentionsOctolens MCPFetch six months of high-relevance mentionsweb_fetchYour MCP clientRead the page being rewritten (built into most MCP clients)
All 23 Octolens MCP tools are documented on the MCP page and in the docs.
The full playbook
Replace the placeholders, then paste this into your agent.
Do the following once:
1. Read the page to rewrite: fetch {PAGE_URL} and note its current headline,
subheadline, and main claims.
2. Call get_workspace to load the company profile, then call list_keywords to
confirm "{BRAND_NAME}" is tracked and get its keyword ID.
3. Call list_mentions to fetch up to 200 mentions of "{BRAND_NAME}" from the
last 6 months with relevance: High. Include positive and neutral sentiment.
Skip mentions authored by the company or its employees (use the workspace
profile to spot them).
4. Extract the customer vocabulary. Go through the mentions and collect,
verbatim:
- the nouns customers use for the product and its category
- the verbs they use for what it does for them
- the problems they say it solved, in their words
- recurring phrases that appear in 3+ independent mentions
Cluster these into themes. For each theme, keep 2–3 exact quotes as
evidence with source URLs.
5. Compare against the current page. List the places where the page's
language and the customers' language diverge — where the page says
"leverage synergies" and customers say "it just tells me when someone
complains".
6. Rewrite using only customer vocabulary:
- 3 headline options (max 10 words each)
- 2 subheadline options (max 25 words each)
- 3 short value-prop bullets
Every line must trace to the extracted vocabulary. No marketing words
that never appear in real mentions.
7. Post the result in chat:
- The vocabulary themes, each with its evidence quotes and links
- The divergence list from step 5
- The rewritten options from step 6, each annotated with which quotes
it's built from
Do not edit any files or publish anything. This is input for a human
copywriter, not a deploy.More skills
Build a wall of love from real mentions
Pull a year of positive mentions, score them against your ICP and homepage positioning, and output a fresh quotes.json for your landing page.
Weekly share-of-voice report
A weekly report on your share of voice vs competitors — counts, sentiment, and what actually drove the changes, with linked evidence.
Turn mentions into Linear issues
Catch bug reports and feature requests posted in public — on Reddit, X, LinkedIn, Hacker News, GitHub — and file them as Linear issues automatically.
Give your agents context on what happens online
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