How to Use the X (Twitter) MCP in Google ADK

Q: What type of user data does Google ADK touch when using X (Twitter)?

When running lookupuserbyusername, the agent collects public metrics like follower counts, bio text, and verification status. All this information is immediately ready to be structured and stored in BigQuery. It keeps the data clean for database ingestion.

Q: Can I use Google ADK to analyze X (Twitter) engagement over time?

Yes, by running gettweetdetails repeatedly and storing the metrics in BigQuery. This lets you run historical trend analysis on a massive scale. You can track how engagement changes week-over-week for specific topics.

Process X (Twitter) data into BigQuery using Google ADK. Enterprise-grade intelligence for your Google Cloud setup.

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Connect X (Twitter) MCP to Google ADK

Create your Vinkius account to connect X (Twitter) 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 X (Twitter) with Google ADK

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Analyze User Profiles via MCP Server

Use `lookup_user_by_username` to pull full X user details, like follower counts and bio text. Your agent can then pass this structured data directly into BigQuery for long-term analysis. It's built for enterprise workflows. You can hold the context of thousands of profiles simultaneously within Gemini models.

Search for Trending Topics (MCP Server)

The `search_recent_tweets` tool searches public X tweets using specific keywords or hashtags, pulling results from the last seven days. This stream feeds right into your Google Cloud pipeline. Instead of just reading a list of tweets, you're adding structured, searchable data to your existing BigQuery tables.

Check Specific Tweet Metrics

Need deep context on one piece of content? The `get_tweet_details` tool gets the text and all engagement metrics for a given tweet ID. This structured output is perfect for loading into BigQuery. Your agent uses this detail to run complex joins against other enterprise data you already own.

Setup guide

Set up X (Twitter) 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 X (Twitter) 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="X (Twitter)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to X (Twitter) 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 X (Twitter). 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 X (Twitter) MCP in Google ADK

The Google ADK lets your agents take X (Twitter) data and immediately act on it within BigQuery. You don't just get a report; you add intelligence to your existing, massive datasets. It’s about making the external social data part of your core business logic.

Absolutely. Because Gemini models support 1M+ tokens, you can feed massive batches of `search_recent_tweets` results into the agent for comprehensive reasoning. You're not limited by context length. This changes how big data analytics is done.

When running `lookup_user_by_username`, the agent collects public metrics like follower counts, bio text, and verification status. All this information is immediately ready to be structured and stored in BigQuery. It keeps the data clean for database ingestion.

Yes, by running `get_tweet_details` repeatedly and storing the metrics in BigQuery. This lets you run historical trend analysis on a massive scale. You can track how engagement changes week-over-week for specific topics.

The MCP Server only provides publicly accessible data. When you use the tools, the agent collects public text and numerical metrics from X (Twitter). This data then moves into your secure, managed environment in BigQuery. Privacy controls are handled by your Google Cloud infrastructure.

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