How to Use the CryptoCompare (Crypto Market Data) MCP in Google ADK

Q: Can I filter tools in Google ADK?

Yes, use the toolnames filter to whitelist only the functions you need. This keeps your agent's capability set focused and predictable.

Connect Google ADK agents to live crypto market data for complex reasoning over BigQuery datasets.

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Connect CryptoCompare (Crypto Market Data) MCP to Google ADK

Create your Vinkius account to connect CryptoCompare (Crypto Market Data) 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 CryptoCompare (Crypto Market Data) with Google ADK

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BigQuery-ready market data for Google ADK

Push live price snapshots from `get_price` into your Google Cloud pipelines. Your Gemini-based agents can now correlate real-time crypto prices with your existing BigQuery tables. This bridge allows your agent to perform reasoning tasks on massive historical sets. Use `get_top_mktcap` to define the universe of assets your agent monitors.

Historical OHLCV analysis in Google ADK

Fetch precise OHLCV logs using `get_historical_minute` to feed your model's long-context window. Your agent processes these sequences to identify volatility spikes. Because the agent runs on Google infrastructure, it handles large data chunks effortlessly. You get clean historical insights without manual data parsing.

Automated news monitoring with Google ADK

Use `get_latest_news` to give your agent a pulse on the market. It consumes news feeds to adjust its bias before executing any logic in your cloud environment. Check `list_news_categories` to ensure your agent focuses on relevant topics. Your agent makes decisions based on the most recent headlines available.

Setup guide

Set up CryptoCompare (Crypto Market Data) 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 CryptoCompare (Crypto Market Data) 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="CryptoCompare (Crypto Market Data)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to CryptoCompare (Crypto Market Data) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about CryptoCompare (Crypto Market Data) MCP in Google ADK

Use the McpToolset class with your server URL inside your Python script. Pass this toolset into the LlmAgent constructor to enable native tool execution.

The framework handles both Stdio and HTTP transports. You choose the one that fits your Google Cloud deployment pattern.

The server acts as a clean conduit for market info. It only processes public financial metrics, ensuring your internal BigQuery data remains isolated from the data feed.

Yes, use the tool_names filter to whitelist only the functions you need. This keeps your agent's capability set focused and predictable.

Gemini models excel at processing these structured responses. The tools return clean JSON that the agent reads and reasons over instantly.

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