AI Prompt to Audit Contentful Entries for SEO Gaps Using Contentful's MCP Server

0 views Updated Oct 13, 2026

This AI prompt helps content teams and SEO specialists use Contentful's MCP server to audit live CMS entries for on-page SEO gaps without manually opening each entry in the Contentful web app. Once an MCP connection lets the model read entry fields (title, meta description, body copy, slug, structured content blocks), this prompt gives it a consistent checklist to apply across every entry so the audit doesn't vary in depth from one page to the next.

The prompt is scoped to the specific SEO fields that live inside a typical Contentful content model: metadata fields, heading structure within rich text fields, internal linking in body content, and slug formatting. It asks the model to report gaps entry by entry rather than giving one vague summary, so a content team can turn the output directly into a punch list of fixes.

Because Contentful content models vary between organizations, the prompt leaves room to name the specific content type and fields being audited, and it asks the model to flag entries where expected fields are simply missing rather than guessing at content that isn't there. For teams restructuring the output into Markdown or JSON for a ticketing system, running the result through Prompt Formatter is a natural next step to turn the audit into a clean, structured handoff document.

Prompt template

prompt-template

347 tokens

ROLE: You are an SEO content auditor reviewing CMS entries retrieved through Contentful's MCP integration. CONTEXT: - Contentful space/environment: [SPACE NAME] - Content type to audit: [CONTENT TYPE, E.G. 'BlogPost'] - Fields that map to SEO elements: [E.G. seoTitle = title tag, seoDescription = meta description, slug = URL slug, body = rich text content] - Number of entries to audit this run: [NUMBER, E.G. 25] TASK: 1. Retrieve the specified number of entries of the given content type via the MCP connection. 2. For each entry, check: title tag field length and presence, meta description length and presence, slug formatting (lowercase, hyphenated, no special characters), heading structure within the body field, and presence of at least one internal link. 3. Report findings entry by entry, referencing each entry's ID or slug. 4. Separate entries with missing fields from entries with fields present but not meeting length or format guidelines. 5. Produce a prioritized list ranking entries by how many issues were found. CONSTRAINTS: - Do not assume a field has content if the MCP response shows it as empty or null; report it as missing. - Do not rewrite or suggest replacement copy; only report the gap. - If an entry is missing the content type's standard SEO fields entirely, flag it as a structural gap rather than a content gap. OUTPUT FORMAT: - Audit Summary (entries checked, total issues found) - Entry-by-Entry Findings (entry ID/slug, issues list) - Structural Gaps (entries missing expected fields entirely) - Priority Fix List (ranked by issue count, highest first)

Want it sharper? Optimize this prompt with Prompt Optimizer, check it with the Prompt Debugger or shorten it with the Token Optimizer.

Example input

example-input

51 tokens

Audit the 25 most recently published entries of content type 'BlogPost' in the 'production' environment. Fields: seoTitle maps to title tag, seoDescription maps to meta description, slug is the URL slug, body is the rich text field.

When to use it

You're doing a periodic SEO health check across a large set of published Contentful entries
You just changed your content model and need to verify existing entries still meet metadata requirements
You're onboarding a new content type and want a baseline audit before it scales to hundreds of entries
You need a prioritized fix list to hand to writers or editors instead of raw entry data

Best practices

Name the exact Contentful content type and field IDs you want audited, since field names vary by content model
Ask the model to flag missing fields explicitly rather than treating an empty field as a minor issue
Request entry-by-entry output with entry IDs or slugs included, so fixes can be traced back to the exact entry
Cap the number of entries per run to a manageable batch so the audit stays thorough instead of skimming

Common mistakes

Asking for a general SEO audit without specifying which Contentful fields map to title tags, meta descriptions, and headings
Running the audit across hundreds of entries at once, which leads to shallow, generic findings
Not asking the model to distinguish between a field that's missing versus one that's filled in poorly
Treating flagged issues as fixes to apply automatically rather than a list for a human editor to review

FAQs

Can ChatGPT or Claude read my Contentful entries without a plugin or integration?

No. Without an MCP connection or another integration, the model only sees whatever text you paste into the chat; it can't browse your Contentful space on its own.

What SEO issues can this type of prompt actually catch versus what it can't?

It can catch structural and formatting gaps, like missing fields, slug formatting, and heading structure, because those are visible directly in the entry data. It can't judge keyword relevance or search ranking potential, since that requires external search data the model doesn't have access to through Contentful alone.

How is auditing via MCP different from exporting a CSV of Contentful entries and pasting it in?

MCP lets the model query entries directly and work with live, structured field data, including nested rich text, whereas a CSV export often flattens or loses that structure, making heading-level and field-type checks less reliable.

What tool pairs well with this prompt for turning the audit into a clean handoff doc?

Prompt Formatter — it's built for converting a wall of findings like this one into structured Markdown or JSON that's easier to hand off to editors or feed into a ticketing system.

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ChatGPT Prompt to Audit Webflow CMS Content for SEO Gaps via Webflow's MCP Server

This ChatGPT prompt for auditing Webflow content reads your actual CMS collection items through Webflow's MCP server and flags SEO gaps miss…

ROLE: You are an SEO content auditor with read access to a Webflow site's CMS collections through an MCP connection.

CONTEXT:
- Site/collection to audit: [COLLECTION NAME, e.g. Blog Posts]
- Target keyword focus: [DESCRIBE PRIMARY TOPICS OR KEYWORDS THE SITE RANKS FOR OR WANTS TO RANK FOR]
- Number of items to review: [NUMBER OR "all"]

TASK:
1. Pull the title, meta description, word count, and slug for each item in the specified collection.
2. Flag items missing a meta description or with one under [NUMBER] characters.
3. Flag items with duplicate or near-duplicate title tags across the collection.
4. Flag items under [WORD COUNT] words as potentially thin content.
5. Rank all findings by severity: critical, moderate, minor.

CONSTRAINTS:
- Only report on data actually retrieved from the collection - do not guess at content you were not given
- Reference each flagged item by its exact slug or URL
- Do not recommend specific rewritten copy unless asked - this audit identifies issues, it does not fix them

OUTPUT FORMAT:
Severity: [critical/moderate/minor]
Page: [slug or URL]
Issue: [specific problem]
Recommended fix direction: [one sentence, no full rewrite]

Content & SEO

AI Prompt to Audit Blog Content for SEO via HubSpot's MCP Server

This HubSpot MCP prompt is for content marketers and SEO leads who want ChatGPT or Claude to pull live blog post data straight out of HubSpo…

ROLE: You are an SEO editor reviewing a blog post pulled from HubSpot via its MCP server.

CONTEXT:
- Post title: [POST_TITLE]
- Post slug: [POST_SLUG]
- Target keyword: [TARGET_KEYWORD]
- Current meta description: [META_DESCRIPTION]
- Word count: [WORD_COUNT]
- Topic cluster / related posts: [RELATED_POST_TITLES]
- Full post body (via HubSpot MCP read): [POST_BODY]

TASK:
Audit this post for on-page SEO issues using only the data above. Check specifically for:
1. Whether the title and meta description match the target keyword's search intent
2. Whether the meta description is within 120-160 characters and includes the target keyword
3. Header structure problems (missing H2/H3s, headers that don't match what the section actually covers)
4. Thin or underdeveloped sections relative to top-ranking content for this keyword
5. Missing internal links to or from the related posts listed above

CONSTRAINTS:
- Do not invent metrics, rankings, or competitor data you were not given
- If a required field above is missing or empty, say so explicitly instead of guessing
- Keep recommendations specific to this post, not generic SEO advice

OUTPUT FORMAT:
- A short summary verdict (Strong / Needs Work / Weak)
- A prioritized list of 3-6 specific fixes, each one sentence
- A separate line listing any internal link opportunities by post title

Content & SEO

AI Prompt to Turn a Blog Post Into a Twitter/X Thread

This AI prompt for repurposing a blog post into a Twitter/X thread is built for content marketers, bloggers, and solo creators who already h…

ROLE: You are a social media copywriter who specializes in turning long-form content into high-performing Twitter/X threads.

CONTEXT:
Source blog post or article: [PASTE FULL BLOG POST TEXT HERE]
Target audience: [DESCRIBE WHO WILL READ THIS THREAD]
Voice/tone to match: [E.G., CASUAL AND DIRECT / PROFESSIONAL AND AUTHORITATIVE]
Goal of the thread: [E.G., DRIVE CLICKS BACK TO THE FULL POST / STAND ALONE AS VALUABLE CONTENT]

TASK:
1. Identify the single most compelling hook, insight, or claim in the source content.
2. Generate 3-5 alternative opening tweets built around that hook, each using a different angle (question, bold claim, contrarian take, specific number/result).
3. Once the opening is chosen, break the rest of the article into a logical sequence of [NUMBER] tweets that build the argument step by step.
4. Write a closing tweet that includes a clear call to action: [E.G., LINK BACK TO FULL POST / ASK A QUESTION / INVITE REPLIES].

CONSTRAINTS:
- Each tweet must stay under 280 characters, including spaces and punctuation.
- Each tweet must make sense as a standalone statement, not require the previous tweet for context.
- Do not use hashtags unless explicitly requested.
- Match the specified voice and tone throughout, not just in the opening tweet.
- Number each tweet (1/, 2/, etc.) at the start.

OUTPUT FORMAT:
First, list the 3-5 candidate opening tweets under the heading "OPENING OPTIONS."
Then, under the heading "FULL THREAD," output the complete numbered thread starting from your recommended opening tweet through the closing call-to-action tweet.