How to Use the Substack MCP in Google ADK

Q: What does the Google ADK need to access for Substack content?

The agent uses getpost and listposts to retrieve the necessary article text and metadata. This data is then available in the 1M+ token context window.

Q: If I use the Google ADK, what specific data does it touch regarding subscribers?

The toolset accesses subscriber records via listsubscribers. This means you're working with structured user identifiers and associated metadata for analysis in BigQuery.

Analyze Substack performance metrics for Google ADK in BigQuery.

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Connect Substack MCP to Google ADK

Create your Vinkius account to connect Substack to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Substack with Google ADK

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Run deep content audits.

The `list_posts` tool fetches a chronological feed of recent articles. With the Google ADK, your agent can ingest this entire history and run it through Gemini for large-context analysis. This lets you identify patterns in publication cadence or topic shifts across thousands of posts.

Process subscriber data at scale.

You use `list_subscribers` to gather all user records. The agent can then feed this massive dataset into BigQuery for cross-referencing with other enterprise data sources you already manage. It’s built for when your Substack analytics need to live alongside your core business metrics.

Get comprehensive publication context.

The `get_publication_info` tool gives foundational details on the entire Substack. This information is perfect for grounding a large-scale agent, ensuring it understands the platform's overall scope before generating content. This helps prevent generalized outputs when you need deep, domain-specific analysis.

Setup guide

Set up Substack 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 Substack 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="Substack_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Substack tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Substack MCP in Google ADK

The MCP Server exposes the raw Substack tools. Your agent integrates these tools directly into your LlmAgent, allowing you to pull data and analyze it within the Gemini ecosystem.

Yes. Because of its native connection points (BigQuery), you can process massive amounts of Substack data that would overwhelm smaller, non-cloud-integrated tools.

The agent uses `get_post` and `list_posts` to retrieve the necessary article text and metadata. This data is then available in the 1M+ token context window.

It's built for it. By streaming results to your agent, you maintain a continuous flow of information that Gemini can reason over deeply and accurately.

The toolset accesses subscriber records via `list_subscribers`. This means you're working with structured user identifiers and associated metadata for analysis in BigQuery.

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