How to Use the Goodreads MCP in Google ADK

Q: How do I restrict the Goodreads MCP tools exposed to my Google ADK agent?

Use the optional toolnames filter when setting up your toolset. This lets you restrict access, ensuring the agent can only run searchbooks and nothing else.

Q: Is my Goodreads reader profile data safe with this server?

Yes, all requests to getuserpublicprofile are executed in transient V8 sandboxes. Your profile details and reading habits are never stored on Vinkius servers, ensuring total privacy.

Run deep literary analysis across millions of tokens by connecting Goodreads to your Google ADK pipelines.

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Connect Goodreads MCP to Google ADK

Create your Vinkius account to connect Goodreads 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 Goodreads with Google ADK

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Long-context Goodreads analysis with Google ADK

This server exposes `list_author_books` to feed entire Goodreads bibliographies into Gemini's million-token context window. The Google ADK passes raw metadata from `get_book_info` straight into the LLM, allowing for deep thematic synthesis across an author's entire life work. You don't have to worry about running out of space when analyzing heavy Goodreads datasets. The native integration with Gemini models means you can map out entire genres in a single reasoning loop.

BigQuery catalog enrichment

This integration uses `search_books` to merge your internal BigQuery warehouse data with live Goodreads book metadata using this MCP Server. Your Google ADK agent can query your database tables for missing ISBNs, then fetch the correct series data via `get_series_metadata` to prepare the SQL update. This keeps your Google Cloud databases clean without manual scripting. The agent handles the lookup, fetches the correct series data, and writes the updates directly.

Enterprise review pipelines

This server relies on `get_user_reviews` to process thousands of Goodreads reader opinions for market research. By connecting your Google ADK agent to this MCP, you can pull qualitative feedback and run sentiment analysis models on Google Cloud. The ADK manages the transport layer, letting you scale your Goodreads queries. You can monitor reader trends by parsing public profiles with `get_user_public_profile` to segment feedback by reader demographics in BigQuery.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Goodreads. 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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Common questions about Goodreads MCP in Google ADK

You initialize the toolset using the MCP server URL. Your agent can then call `get_user_shelves_list` to fetch the target shelf data.

Yes, by combining the toolset with Gemini's parallel tool calling. You can dispatch multiple concurrent requests to `get_author_profile` to map out your database quickly.

Use the optional `tool_names` filter when setting up your toolset. This lets you restrict access, ensuring the agent can only run `search_books` and nothing else.

The SDK handles transport retries automatically over both Stdio and HTTP. If `get_book_info` fails due to a network hiccup, the framework retries the request before throwing an error.

Yes, all requests to `get_user_public_profile` are executed in transient V8 sandboxes. Your profile details and reading habits are never stored on Vinkius servers, ensuring total privacy.

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