Supercharge your AI with Wallabag. Organize, annotate, and retrieve private articles.
Works with every AI agent you already use
…and any MCP-compatible client
Connect to your AI in seconds.
Wallabag MCP lets you build a private, self-hosted knowledge base for web articles. Save URLs from any site, manage them with tags, and pull out clean text content directly into your AI agent's workspace.
What your AI can do
Add tags to entry
Attaches one or more tags to an existing saved article entry.
Create annotation
Adds a specific note or annotation to an article entry.
Create entry
Saves a new web URL and its associated content into your Wallabag library.
Save any article URL to your private Wallabag instance.
Apply tags and mark entries as read or favorite, keeping your knowledge base organized for later recall.
Retrieve the full text of an article along with any existing user annotations or notes.
List, add, and remove tags from specific entries to categorize your research material.
Remove old or irrelevant articles entirely from your library.
Ask an AI about this
Compatible AI Apps
OAuth 2.0 CompatibleWaiting for input…
Wallabag (Pocket Alternative) Has 11 Tools
Use these tools to manage the entire lifecycle of saved articles, from initial saving and tagging to deep content retrieval and deletion.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Wallabag (Pocket Alternative) on VinkiusAdd Tags To Entry
Attaches one or more tags to an existing saved article entry.
Create Annotation
Adds a specific note or annotation to an article entry.
Create Entry
Saves a new web URL and its associated content into your Wallabag library.
Delete Entry
Permanently removes an article entry from your saved collection.
Get Entry
Retrieves the full details and content for a single, specific article ID.
List Annotations
Fetches all notes or annotations associated with an entry ID.
List Entries
Returns a list of metadata for every article currently stored in your Wallabag account.
List Tags
Shows all the tags you have created and used across your entire library.
Mark Entry Favorite
Changes an article's status to 'starred,' flagging it as a key reference piece.
Mark Entry Read
Archives an entry, marking it as fully processed or consumed.
Remove Tag From Entry
Removes a specific tag from an article entry that currently has it.
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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 Wallabag (Pocket Alternative), then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Wallabag. 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 connection provides 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The Manual Article Archive Mess
Right now, if you want to save something good from the web, you're clicking through three different services: a bookmarking tool, a note-taking app, and maybe a separate file folder. You copy the URL into one place, paste the highlights into another, and manually write down what it was about in a third spot. It’s a mess of tabs, copy-pasting, and fragile manual indexing.
With this MCP, you save the article link once. When your agent pulls that content, it gives you clean text—no ads, no distractions. You get one source of truth for the article, ready to be summarized or analyzed without any painful copy/paste steps.
Retrieving Full Article Content
Manually checking each bookmarking service means you might get a snippet or a partial view. You spend time opening multiple tabs just to verify if the full content is available and readable.
Now, an agent call can fetch the clean, extracted text directly for you. It’s not just a link; it's the actual material, instantly ready for analysis.
What your AI can actually do with this
You can connect your own Wallabag instance to your preferred AI client to turn scattered bookmarks into structured research assets. Instead of relying on proprietary services, you keep all your article data private, right where you need it. You get the ability to list every saved article and fetch its full, clean text content—perfect for analyzing sources without ads or distraction.
Beyond just saving links, you can annotate articles by creating specific notes or highlights directly within the agent's workflow. This system also lets you organize your research with tags and manage reading status, marking entries as read or starring favorites. The real power comes when you combine this MCP with others: you can chain a messaging tool with this one to automate summaries based on newly saved articles, all through Vinkius’s multi-MCP chaining capabilities.
You just connect once from your AI client, and the entire catalog becomes available.
019e3908-6442-7375-8cae-607f093accb2 Here's how it actually works
The bottom line is, you tell your agent what article you need, and it handles the connection and retrieval using your stored credentials.
Subscribe to this MCP and provide the necessary credentials for your private Wallabag instance.
Your AI client connects through Vinkius, authenticating access via a zero-trust proxy. Your keys never sit on a disk.
You can then instruct your agent to list all articles or save a new URL, accessing the data instantly.
Who is this actually for?
Researchers and deep-dive analysts who manage hundreds of sources. This is for anyone whose daily job involves reading things they don't have time to read immediately, but need to remember later.
Needs to save dozens of journal articles and annotate key findings across multiple sources before writing a literature review.
Requires an organized way to capture ideas from industry blogs and news sites, needing specific tags like 'Q3-campaign' or 'SEO-pillar'.
Needs a personal repository for best practices and reference material that must remain private and easily searchable.
What Changes When You Connect
Avoids data lock-in. Since you use your own instance, your research remains yours, protected by the zero-trust proxy in Vinkius.
Deep analysis is easier with clean text. You can get the extracted text of any article without ads or formatting junk, making it perfect for AI summarization.
Stay on top of everything using mark_entry_favorite. Instead of forgetting a key source, you flag it and retrieve it later via an agent call.
Maintain structure with tags. Use list_tags to see your full taxonomy, then use add_tags_to_entry to apply consistent categorization across hundreds of articles.
Control your data flow. The ability to run a get_entry allows you to pull the raw content for deep processing, or call list_annotations to review specific user notes.
See it in action
Synthesizing competitive intelligence
A marketing manager saves 30 articles on competitor launches. She asks her agent to use list_entries and then filter them by tag 'competitor-X' before running a summary, giving her one document summarizing all key product features mentioned.
Reviewing academic literature
A student needs to find every source related to 'quantum computing'. She calls list_tags to check available categories and then uses the agent to fetch content for all entries tagged 'Physics' before asking it to summarize the common themes.
Cleaning up old sources
After a project ends, an engineer needs to remove dozens of irrelevant links. She runs list_entries and then uses the agent to systematically call delete_entry for all articles older than six months.
The honest tradeoffs
Relying on generic search
Searching a cloud-based bookmarking tool and getting results mixed with general web noise, forcing manual verification of source reliability.
Use list_entries to see only your curated sources. If you need notes attached, use the agent to call get_entry then follow up with list_annotations for a complete picture.
Over-tagging or miscategorizing
Manually adding too many tags like 'important,' 'read later,' and 'todo' because you don't trust the system to keep track.
Establish a strict tag taxonomy first by calling list_tags. Then, use add_tags_to_entry sparingly, only for high-level categories.
Losing key insights
Saving an article but forgetting to highlight the crucial sentence or add a note about who needs to read it next.
Immediately after saving content via create_entry, use create_annotation to document your immediate thoughts before you move on.
When It Fits, When It Doesn't
Use this MCP if your primary need is private, structured knowledge management for web articles. If your workflow requires complex filtering based on fields not available in the current tools (e.g., 'find all entries containing the word X AND tagged Y'), you might need a different type of indexing service. However, if you are dealing with source material that needs to be read-it-later and annotated before analysis, this is perfect. Don't use it just because you bookmark things; use it because you plan to analyze them later. It’s not a replacement for reading—it’s an organized waiting room for your ideas.
Questions you might have
How do I add tags to entries using the add_tags_to_entry tool? +
You tell your agent the ID of the entry and the specific tag name(s). It executes the update, linking that metadata directly to the article record.
Can I get annotations for a specific article using list_annotations? +
Yes. You provide the Entry ID, and the agent pulls all existing notes or highlights associated with it from your Wallabag instance.
What is the difference between create_entry and get_entry? +
Using create_entry saves a brand new URL to your library. Using get_entry fetches all the existing information for an article you already saved by its unique ID.
How do I mark an article as read using mark_entry_read? +
You instruct the agent with the Entry ID, and it updates your Wallabag status, archiving the article so it's filtered out of your 'to-read' list.
I need to delete an article. How does the `delete_entry` tool work? +
The tool permanently removes the entry from your Wallabag library. You must pass a specific Entry ID; this ensures you only wipe out the content you intend to remove.
How can I get an overview of all my saved articles using `list_entries`? +
It pulls a list of every article ID and its basic metadata from your library. This is helpful for quickly seeing the scope of your backlog or identifying entries that need reviewing.
I want to flag an important article but it's not finished yet. How do I use `mark_entry_favorite`? +
It instantly changes an entry’s status, marking it as a favorite or 'starred.' You only need the Entry ID for this simple organizational update.
I used `add_tags_to_entry`, but now I need to correct the tags. How do I use `remove_tag_from_entry`? +
This tool deletes a specific tag from an entry, allowing you to correct classification errors or update research categories after your initial tagging.
Can I save a new article just by providing a URL? +
Yes! Use the create_entry tool with the URL you want to save. Your agent will add it to your Wallabag account immediately.
How do I archive an article once I've finished reading it? +
Simply ask the agent to mark the article as read using the mark_entry_read tool with the specific Entry ID.
Can I see the highlights and notes I've made on an article? +
Yes. The list_annotations tool retrieves all highlights and notes associated with a specific Entry ID, allowing the AI to reference your personal insights.
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