Contentful Graphql

@Contentful-Graphql fetch all blog posts published in the last 30 days with their authors and tags

This example would return a GraphQL response containing a collection of blog post entries from your Contentful space. Each entry would include fields like title, publishDate, slug, plus nested author references (name, bio, avatar) and associated tag arrays. The response structure mirrors your content model's schema. In a real query, you'd see 0-N posts depending on your publishing cadence, formatted as JSON with sys metadata and localized field values.

This demonstrates the GraphQL Content API Query tool's read capabilities. You'll need a valid CMA token (obtained via the Get CMA Token tool first) and must specify your space ID and environment. The query syntax must match your Contentful content model exactly—field names, content type IDs, and reference structures are space-specific. No write permissions required for read-only queries.

@Contentful-Graphql register this product search query as a persisted query so our frontend can call it by hash instead of sending the full query text

This example would demonstrate registering a GraphQL query (e.g., a product catalog search with filters) and receiving back a SHA256 hash identifier. On the first call, you'd provide the complete query text—something like filtering products by category, price range, and availability. Contentful would return a hash like 'a3f2b9c8...' that your application can subsequently use to execute the same query without transmitting the full query string, reducing payload size and improving cache efficiency.

This showcases the GraphQL Content API Persisted Query tool's optimization workflow. Persisted queries improve performance and security by allowing hash-based query execution. After registration, your frontend calls the query by hash alone. Note this is a two-step process: first register with full query text, then execute by hash. Useful for production apps with repeated query patterns, but adds complexity for one-off queries.

@Contentful-Graphql pull all landing page entries with their SEO metadata and hero images, then draft a content audit report highlighting pages missing alt text or meta descriptions

This example would first execute a GraphQL query fetching landing page content types with fields for metaTitle, metaDescription, heroImage, and imageAlt. The response might show 15 landing pages, with the AI then analyzing which entries have null or empty SEO fields. The resulting audit report would list pages by title and URL slug, flagging 4 pages missing meta descriptions and 2 with hero images lacking alt text, plus recommendations for remediation prioritized by page traffic or importance.

This synthesis example combines the MCP's data retrieval with AI reasoning to produce actionable insights. It demonstrates how GraphQL's flexible querying lets you fetch exactly the fields needed for content audits, accessibility reviews, or governance checks. Requires read access to your content model and assumes your schema includes SEO/accessibility fields. The AI interprets the structured data but can't write back fixes—you'd handle updates separately via Contentful's UI or CMA.

When Contentful GraphQL beats manual CMS checks for content teams

A 5-person marketing team publishes weekly blog posts and landing pages through Contentful. They need to verify live content matches what editors approved before pushing social links. This MCP wins here because the GraphQL Content API Query tool pulls exact published state in seconds—no logging into the CMS, no screenshot comparisons. The persisted query tool speeds up repeated checks (same query, different dates). Trade-off: if your team only updates content monthly, the setup overhead isn't worth it. But for weekly or daily publishing cycles, this MCP turns a 10-minute manual check into a 30-second AI query. Worth the API key if you're publishing more than twice a week.

Why this MCP works for support teams with structured help docs

A 3-person support team answers 40 tickets daily, most requiring links to help articles stored in Contentful. They waste 15 minutes per day searching the CMS for the right doc URL. This MCP is the right call because the GraphQL query tool lets the AI search by title, tag, or keyword and return the live URL instantly. The CMA token tool ensures queries hit the latest published version, not a stale cache. Limitation: if your help docs live across multiple systems (Contentful plus Notion plus Google Docs), this MCP only covers the Contentful slice. But if Contentful is your single source of truth for customer-facing content, this MCP pays back setup time in the first week.

When Contentful GraphQL handles QA for product launches

A 6-person product team launches a new feature every quarter, requiring coordinated updates across 20+ marketing pages, changelog entries, and FAQ sections in Contentful. Two days before launch, they need to confirm every page references the correct feature name and pricing tier. This MCP is overkill for ad-hoc checks but essential for repeatable launch checklists. The persisted query tool caches the audit query (search all pages for feature name, return mismatches), so the same check runs in under 10 seconds each launch cycle. If your launches happen less than quarterly, manual spot-checks are faster. But for teams shipping monthly or faster, this MCP turns a 2-hour manual audit into a 5-minute AI-driven report.