How to Use the Wallabag (Pocket Alternative) MCP in OpenAI Agents SDK

Q: How does the OpenAI Agents SDK handle searching Wallabag (Pocket Alternative)?

Your agent uses listentries to pull a master list of article IDs. It then passes those IDs into other tools, like getentry, so it can analyze the stored content against your query.

Q: What happens if I need to delete an item with OpenAI Agents SDK?

You simply call deleteentry and pass the entry ID. The agent executes this action, making sure that piece of content is removed from your self-hosted Wallabag (Pocket Alternative) list.

Q: Can I mark articles as read using OpenAI Agents SDK?

Yes, you use markentryread. This tool changes the status of a specific entry ID, effectively archiving it in your MCP Server. It helps keep your active list clean.

Q: How do I tag articles when using OpenAI Agents SDK?

The agent uses addtagstoentry or removetagfromentry. You pass the entry ID and the desired tags to keep your Wallabag (Pocket Alternative) collection organized.

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Connect Wallabag (Pocket Alternative) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Wallabag (Pocket Alternative) to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Wallabag (Pocket Alternative) with OpenAI Agents SDK

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Capture and manage articles in `create_entry`

When you hit a useful article, your agent saves it instantly using `create_entry`. This tool takes a URL and stores the content in your self-hosted read-it-later list. You won't lose anything good; it’s locked down right away. This capability lets your production agent system build an organized knowledge base. It makes sure that articles, even if they change later, remain accessible for future analysis.

Annotate and organize entries with `create_annotation`

Need to remember a specific point from an article? Your agent creates annotations using the `create_annotation` tool. You can tag that exact spot in the text, so it's not just another link. It’s great for deep research tasks. After reading several sources, your AI client pulls together all those little notes and specific insights you captured via this MCP Server.

Reviewing saved content with `list_entries`

Want to see everything you've bookmarked? The agent runs `list_entries` to pull a list of all stored articles. It gives you the titles and IDs for every piece of content in your Wallabag (Pocket Alternative) collection. You can then use other tools, like `get_entry`, to fetch the actual article body, giving you full context without having to search through bookmarks.

Setup guide

Set up Wallabag (Pocket Alternative) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Wallabag (Pocket Alternative) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Wallabag (Pocket Alternative) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Wallabag (Pocket Alternative) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Wallabag (Pocket Alternative) Agent",
            instructions="You have access to Wallabag (Pocket Alternative) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Wallabag (Pocket Alternative) MCP in OpenAI Agents SDK

Your agent uses `list_entries` to pull a master list of article IDs. It then passes those IDs into other tools, like `get_entry`, so it can analyze the stored content against your query.

You simply call `delete_entry` and pass the entry ID. The agent executes this action, making sure that piece of content is removed from your self-hosted Wallabag (Pocket Alternative) list.

Yes, you use `mark_entry_read`. This tool changes the status of a specific entry ID, effectively archiving it in your MCP Server. It helps keep your active list clean.

The agent uses `add_tags_to_entry` or `remove_tag_from_entry`. You pass the entry ID and the desired tags to keep your Wallabag (Pocket Alternative) collection organized.

This MCP Server handles Entry URLs, annotations, and associated tags. The agent processes these pieces of content when it interacts with the Wallabag (Pocket Alternative) tools.

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