How to Use the Hashnode MCP in Google ADK

Q: How does Google ADK restrict Hashnode access?

You use the toolnames filter. This lets you expose the read tools while blocking write access for specific agents.

Connect Gemini's massive context window to your Hashnode blog using Google ADK to analyze and generate enterprise technical content.

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Connect Hashnode MCP to Google ADK

Create your Vinkius account to connect Hashnode 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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Setup Hashnode with Google ADK

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Long-context blog generation

The Hashnode MCP server lets your Gemini agents read and write directly to your developer blog. You can feed an entire codebase into the model, and it will use `create_post` to publish a detailed architectural breakdown. Google ADK thrives on massive context. Instead of generating short generic posts, your agent can pull hundreds of existing articles using `get_publication_posts` to ensure the new draft perfectly matches your enterprise engineering guidelines.

BigQuery to blog pipeline

Enterprise teams don't write tutorials in a vacuum. By combining this MCP toolset with Google Cloud infrastructure, your agent can pull live usage metrics from BigQuery and turn them into public case studies. Once the data is analyzed, the agent calls `update_post` to refresh your existing articles with the latest performance numbers. Your technical content stays accurate without a human engineer manually editing markdown tables.

Author profiling at scale

Managing a multi-author engineering blog is a massive headache. Your system can run `get_user` to check the credentials and bios of guest writers before allowing them to publish. It then uses `get_post` to review their draft submissions. If the content doesn't meet your Vertex AI safety filters, the agent rejects the draft before it ever hits the live publication.

Setup guide

Set up Hashnode 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

Install Google ADK

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

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

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 Hashnode 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="Hashnode_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Hashnode tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Hashnode MCP in Google ADK

You wrap the endpoint URL in StreamableHttpServerParameters. Then pass it into McpToolset and hand that to your LlmAgent initialization.

Yes. Because Gemini holds over a million tokens, it can ingest everything returned by the list tools and still have room for your enterprise data.

You use the tool_names filter. This lets you expose the read tools while blocking write access for specific agents.

Yes, it does. The SDK supports both Stdio and HTTP transports, so you can test your publishing pipelines locally before deploying to Google Cloud.

Your agent only accesses specific blog articles and user metadata. The MCP Server acts as a strict passthrough for markdown content and author bios, leaving your Vertex AI training data completely isolated.

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