How to Use the Meltwater MCP in Google ADK

Q: Can my Google ADK agent analyze Meltwater search queries alongside my BigQuery data?

Yes. Your agent can pull live analytics using getsearchanalytics and combine that data with your internal tables in BigQuery, giving you a complete view of how media coverage impacts your business.

Q: How do I restrict which Meltwater tools are exposed to my Google ADK agent?

You can use the optional toolnames filter when initializing your McpToolset in Python. This lets you restrict your agent to only read-only tools like listsavedsearches if you want to prevent it from modifying tags.

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Connect Meltwater MCP to Google ADK

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Setup Meltwater with Google ADK

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Long-Context Media Analysis with Google ADK

With `list_content_exports`, your Gemini-powered agents can ingest huge batches of media data and analyze them all at once. Gemini's million-token context window means you can feed entire export files directly to the model for deep thematic analysis. The agent can use `get_media_insights` to extract high-level trends across thousands of articles without losing the nuance. This allows your enterprise agents to spot subtle shifts in public perception that traditional, shorter-context models completely miss.

Enterprise BigQuery Integration for Media Data

By querying live media mentions using `search_content`, your Google ADK agents can write structured payloads straight into BigQuery. This makes it simple to cross-reference brand sentiment with actual sales revenue or website traffic. By using `list_media_sources` to identify where your coverage is coming from, your agent can flag which publications drive the most valuable business outcomes. It turns raw PR metrics into actionable business intelligence.

Automated Folder and Tag Management

The `list_folders` and `list_tags` tools allow your agents to organize incoming brand mentions without manual work. The agent can evaluate the content of a mention and assign the correct taxonomy in real time. When combined with `get_mention_details`, your agent can inspect the specific metadata of a news story and file it away under the right internal category. It keeps your PR team's workspace organized without requiring them to tag articles manually.

Setup guide

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

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Common questions about Meltwater MCP in Google ADK

Yes. Your agent can pull live analytics using `get_search_analytics` and combine that data with your internal tables in BigQuery, giving you a complete view of how media coverage impacts your business.

Gemini can process millions of tokens, allowing your Google ADK agent to pull massive datasets via `list_content_exports` and analyze the entire batch in a single run, rather than breaking it up into small, disconnected chunks.

You can use the optional `tool_names` filter when initializing your `McpToolset` in Python. This lets you restrict your agent to only read-only tools like `list_saved_searches` if you want to prevent it from modifying tags.

Yes. You can connect your Google ADK agent to this MCP Server using either local Stdio transport for development or HTTP transport via Vinkius for secure, cloud-hosted production deployments.

Your API keys are injected at runtime and never saved. The Vinkius sandbox executes the server ephemerally, ensuring that sensitive media mention details and search configurations are processed in memory and never written to persistent storage.

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