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How to Use the WordPress MCP in Google ADK

Build massive-scale WordPress automation in Google ADK by connecting to BigQuery and Vertex AI.

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Google ADK

Connect WordPress MCP to Google ADK

Create your Vinkius account to connect WordPress to Google ADK 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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List Core WordPress Content Types with MCP Server

The `list_blog_posts` tool pulls every blog post from the site, giving you a complete inventory. Similarly, the `list_static_pages` function helps map out all the main informational pages. If you need to see how the content is organized, run `list_blog_categories`.

Deeply Analyze Post Details using MCP Server

Need more than just a title? The `get_post_details` tool fetches every piece of metadata associated with one post. This rich data is perfect for feeding into Vertex AI models. You can also examine site users and their roles by calling `list_site_users`.

Control WordPress Site Structure via MCP Server

Use the `create_new_post` tool to generate content that needs immediate tracking. This is useful for automating documentation updates. Want to clean up old posts? The `delete_post` action removes unwanted entries from the system.

Setup guide

Set up WordPress MCP in Google ADK

Prerequisites

  • Python 3.10+ installed
  • google-adk package (pip install google-adk)
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install Google ADK

    Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.

  2. 2

    Connect via SSE transport

    Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

  3. 3

    Create an LlmAgent

    Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.

  4. 4

    Run with any Gemini model

    The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with WordPress tools in your ADK agent.

agent.py
from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="WordPress_agent",
    model="gemini-2.0-flash",
    instruction="You have access to WordPress tools via MCP.",
    tools=mcp_tools,
)

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WordPress. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about WordPress MCP in Google ADK

You integrate the MCP Server object into your LlmAgent. This allows Gemini models to read structured data—like a list of posts from `list_blog_posts`—and take actions on it.
The agent framework supports long-context reasoning with Gemini, allowing you to process and reason across vast datasets, connecting site data directly to BigQuery tables.
Yes. The agent can list posts (`list_blog_posts`) and then create new ones (`create_new_post`). It's designed for enterprise scale, not just small tasks.
The server exposes site user data (usernames/emails via `list_site_users`), as well as post titles and slugs. This lets your agent audit access controls.
Yes. Since you are building enterprise agents on Google Cloud, the setup natively supports BigQuery integration and handles large volumes of data consistently.

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