ButterCMS MCP. Analyze your content and taxonomy with natural chat.
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ButterCMS. Connect your headless publishing data to your AI agent. This server lets your client search your entire blog, extract content taxonomy (tags, authors, categories), and pull structured data from custom pages.
Get all your content metadata and articles without opening the CMS console or writing complex API calls. It's your entire content warehouse, ready for AI analysis.
What your AI agents can do
Get page layout
Retrieves the exact structural data defining how a specific page is routed and laid out.
Get post details
Pulls all content and metadata for a specific blog post using its slug.
List blog posts
Lists the available routing spaces and titles for all blog posts.
Find an article's full content, metadata, and structure using its slug or full-text keyword search.
List all configured categories, tags, authors, and global content collections to understand the site's full taxonomy.
Retrieve explicit, structured JSON data for custom page configurations across different website layers.
Identify and enumerate all blog posts, either by general listing or specific search criteria.
Determine the exact structural layout and routing information for any given page.
Check the relationships between authors, categories, and tags to identify content gaps or connections.
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ButterCMS MCP Server: 10 Tools for Content & Taxonomy
Use these tools to let your AI agent explore your entire content warehouse, mapping everything from blog posts to custom structured data.
019d7566get page layout
Retrieves the exact structural data defining how a specific page is routed and laid out.
019d7566get post details
Pulls all content and metadata for a specific blog post using its slug.
019d7566list blog posts
Lists the available routing spaces and titles for all blog posts.
019d7566list butter authors
Gets a list of all authors configured in the CMS.
019d7566list butter categories
Lists all main content categories and their unique identifiers.
019d7566list butter tags
Maps and lists every tag used across the entire content system.
019d7566list custom pages
Inspects and lists the existence and structure of all custom-built pages.
019d7566list global collections
Lists structured content groups and their names across the site.
019d7566search blog posts
Performs a full-text search across all blog posts to find articles matching specific keywords.
019d7566search collection field
Finds specific data points within content collections that match given filter criteria.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with ButterCMS, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
ButterCMS MCP Server - Content API for AI
Your AI client connects directly to your entire content warehouse. You can let your agent search your blog, pull content tags, authors, and categories, and grab structured data from custom pages. You get all the content metadata and articles you need without opening the CMS console or writing complicated API calls.
It's your whole content system, ready for your agent to analyze.
Find and Analyze Specific Articles
Your agent uses get_post_details to pull all content and metadata for a specific blog post using its slug. search_blog_posts lets it perform a full-text search across all posts to find articles matching specific keywords. You can also list all available routing spaces and titles for every blog post using list_blog_posts.
Map Out Your Site's Content Structure
Your agent uses list_butter_categories to pull a list of all main content categories and their unique identifiers. It uses list_butter_tags to map every tag used across the entire system. list_butter_authors gives it a list of all authors configured in the CMS. To see structured content groups, it runs list_global_collections to list those groups and their names.
You can inspect the existence and structure of all custom-built pages by calling list_custom_pages.
Deep Dive into Custom Models and Layouts
Your agent uses search_collection_field to find specific data points within content collections that match given filter criteria. get_page_layout retrieves the exact structural data defining how a specific page is routed and laid out. You can also pull a list of all structured content groups using list_global_collections.
How It Works
Your agent uses the MCP tools—like search_blog_posts or list_butter_categories—to query the data. The server sends the content data directly to your AI client. You'll get high-grade content data without having to do manual database lookups or map specific Markdown text yourself. It's all there, ready for your agent to use.
How ButterCMS MCP Works
- 1 Subscribe to the server and input your ButterCMS API Token from your project's security settings.
- 2 Your AI agent calls a discovery tool (like
list_butter_categories) to map the site's overall structure. - 3 The agent uses the resulting metadata to perform a targeted query (like
search_blog_posts) and gets the full content directly into your client.
The bottom line is that your AI client treats your entire CMS as a single, queryable data source, eliminating the need for manual API orchestration.
Who Is ButterCMS MCP For?
Content Writers who need to rapidly check if a keyword is already covered; SEO & Marketing Teams who need to audit tag structures for backlinking proposals; Headless Engineers who debug component failures by inspecting raw JSON page models.
Runs rapid scans to identify if an article covering a target keyword already exists in the knowledge base.
Evaluates existing tag structures and locates authors' articles to propose backlinking opportunities.
Debugs component structures by identifying why specific custom page hits aren't rendering the nested JSON output correctly.
What Changes When You Connect
- Get full content intelligence instantly. Use
get_post_detailsto pull an article's entire body and metadata, not just a snippet. - Map your entire site structure. Run
list_butter_categoriesandlist_butter_tagsto see every content grouping and tag without manual browser checks. - Debug complex layouts quickly. Call
get_page_layoutto verify the exact UI routing and component structure for any page. - Audit content gaps efficiently. Use
search_blog_poststo search for keywords like 'machine learning' across all posts and see what's missing. - Handle custom data types.
list_custom_pageslets you inspect structured JSON models for pages that don't fit standard blog post rules. - See all relationships. Running
list_butter_authorsgives you a definitive list of who wrote what, making it easy to propose author-based content flows.
Real-World Use Cases
Auditing Content Overlap
The SEO team needs to know if they already wrote about 'AI ethics' before starting a new piece. They ask their agent to run search_blog_posts for 'AI ethics'. The agent returns two explicit hits, confirming the topic is covered, saving the writer time.
Debugging a Page Component
A Headless Engineer finds a custom page isn't showing its nested data. Instead of clicking through the CMS console, they use list_custom_pages and inspect the raw JSON structure. This reveals the missing field mapping error immediately.
Building a Content Map
The developer needs to know all content types. They run list_global_collections and list_butter_categories to get a comprehensive map of every structural area, which they can then use to build a data visualization dashboard.
Finding Related Content
A writer wants to propose a backlink from a new article. They ask the agent to run list_butter_authors and list_butter_tags to find all relevant authors and existing tags, ensuring the new content fits the site's existing taxonomy.
The Tradeoffs
Manual API Chaining
The developer writes a script that calls list_blog_posts first, then loops through the results to call get_post_details for every single slug. This is slow and complex to manage.
→ The agent handles this automatically. Just ask the client to 'Find the details for all posts tagged 'AI'. The agent internally manages the calls (list -> filter -> detail) and returns the structured data.
Over-reliance on the UI
The developer tries to figure out the schema by clicking through the CMS admin console, copying text, and pasting it into a spreadsheet. This is error-prone and impossible to scale.
→
Use list_butter_categories and list_butter_tags. These tools extract the raw, machine-readable data structure directly, skipping the visual layer entirely.
Assuming a Single Endpoint
The developer assumes there is one function, like get_all_content(), that returns everything. This doesn't exist, and the API would fail or return incomplete data.
→
Use a combination of tools. Start by calling list_global_collections to identify the content type, then use search_collection_field to drill down into the specific data points.
When It Fits, When It Doesn't
Use this if your job is mapping or auditing content structure. You need to know what content exists, how it's categorized, and what the raw data schema looks like. You're building a dashboard, writing a large knowledge base, or debugging component rendering. Don't use this if you just need to publish a single, simple blog post—that's the CMS's job. If you only need to read a single piece of content and its metadata, get_post_details is enough, but if you need the context (like the categories it belongs to), you need the full suite of tools.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ButterCMS. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Figuring out your site's content structure used to take hours of clicking.
Before this, you had to jump between the CMS dashboard, the tag list, and the category hierarchy. You'd manually copy slugs, open a dozen tabs, and piece together the full site map. It was slow, and you always missed the deep metadata.
Now, your agent connects to ButterCMS. You just ask it to map the content. It runs `list_butter_categories` and `list_butter_tags` and dumps the clean, machine-readable structure directly to your chat. You get the entire site map, instantly.
ButterCMS MCP Server: Content & Taxonomy
You no longer need to run ad-hoc database queries or write custom scripts just to check if an article lives in a specific collection or if a tag structure is consistent. Your AI agent handles that complexity by running tools like `list_global_collections` and `search_collection_field`.
This means you move from debugging data points one by one to simply asking, 'Show me everything related to X.' It’s a structural shift in how you work.
Common Questions About ButterCMS MCP
How do I use the `search_blog_posts` tool to find articles? +
You simply tell your agent to search for keywords. The agent runs search_blog_posts and returns all matching article slugs and snippets. It's great for seeing if a topic is already covered.
Can I find all authors using `list_butter_authors`? +
Yes. The tool runs a check against the CMS and returns a full list of every author configured. You can then ask the agent to find articles published by a specific author.
What is the difference between `list_butter_categories` and `list_global_collections`? +
Categories define the site's primary content groupings. Global collections define structured sets of content items that might span multiple content types.
Does `get_post_details` require a slug? +
Yes, it requires the article slug. You provide the slug, and the tool pulls the full, rich content—including body, metadata, and structure.
How do I check the page structure using `get_page_layout`? +
You provide the URL or slug, and the tool returns the explicit structural data. This is crucial for debugging component rendering in a headless environment.
How do I check all content groupings using the `list_butter_tags` tool? +
The list_butter_tags tool retrieves every global taxonomy tag defined in ButterCMS. You can use this to map all possible content boundaries and see how authors group their articles across the site.
What does `list_global_collections` do, and when should I use it? +
list_global_collections enumerates structured JSON objects that track content across multiple website layers. Use this when you need to know the formal structure of content collections, not just the general categories.
If I want to find articles about a specific topic, should I use `search_blog_posts` or `search_collection_field`? +
search_blog_posts searches the full text of published blog articles using keywords. Conversely, search_collection_field lets you filter content by specific structured fields attached to collections.
Can my AI analyze the full content of an already published article? +
Absolutely. Through the generic get_post_details tool using a targeted URL slug, the agent actively retrieves the article content boundaries directly from ButterCMS, letting you check formatting errors inside the markup natively.
Does it connect out to our Custom Pages or specifically built Collections? +
Yes. Beyond just basic blog articles, the agent explores specific array bounds pulling the 'list_custom_pages' endpoint isolating pure localized architectures and fields.
Will it discover writers or tags independently mapping cross-relations? +
Yes! Running standard category lists returns an exact enumeration mapping taxonomy arrays straight into conversational strings. Ask the bot 'Who are our CMS authors?' and watch the results.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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