How to Use the Covalent MCP in Google ADK

Q: How does Covalent data interact with Vertex AI via Google ADK?

The agent calls listsupportedchains to fetch network metadata, which Vertex AI processes to plan downstream tasks. The model uses its reasoning capabilities to decide whether to query getblockdetails based on your prompt.

Feed multi-chain data directly to Google ADK enterprise agents with native Vertex AI and BigQuery integrations.

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Connect Covalent MCP to Google ADK

Create your Vinkius account to connect Covalent 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 Covalent with Google ADK

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Enrich BigQuery Warehouses via Google ADK

`get_token_balances` fetches real-time wallet holdings across 100+ chains to update your Google ADK datasets instantly. This MCP integration allows your enterprise agents to join live on-chain balances with historical off-chain customer profiles in BigQuery. Your agent can run complex SQL queries on cold storage data, then call `get_chains_status` to check if the live chain state matches your warehouse records. This keeps your financial reports accurate to the latest block.

Analyze Deep Transaction Logs in Gemini Long Context

`get_transaction_details` extracts raw log events and decoded parameters, feeding them directly into the Gemini 1M+ token context window. This massive context allows your Google ADK agent to analyze thousands of transactions simultaneously without running out of memory. The agent processes these logs alongside outputs from `get_token_transfers` to map complex multi-hop token movements. You get a complete, natural language summary of smart contract interactions generated directly inside Vertex AI.

Monitor Liquidity Pools for Enterprise Portfolios

`get_dex_pools` resolves reserve amounts, token pairs, and volume metrics for specific decentralized exchanges. Your Google ADK agent uses this data to assess market depth before recommending portfolio adjustments. By combining this with `get_historical_portfolio`, the agent tracks how your liquidity positions perform over time. The MCP Server automatically formats these metrics into clean payloads ready for your Google Cloud dashboard.

Setup guide

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

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Common questions about Covalent MCP in Google ADK

Install the core library and define your Vinkius endpoint using our hosted MCP toolset. Pass this toolset into your `LlmAgent` configuration to expose tools like `get_nft_balances` to Gemini.

Yes, use the filtering parameters in your config. For example, you can expose only `get_token_balances` while blocking transaction-level tools to keep your MCP Server access strictly controlled.

The agent calls `list_supported_chains` to fetch network metadata, which Vertex AI processes to plan downstream tasks. The model uses its reasoning capabilities to decide whether to query `get_block_details` based on your prompt.

Yes, Google ADK manages parallel tool execution natively, allowing Gemini to query `get_transactions` for multiple wallets at once. The responses are streamed directly into your agent's memory.

All wallet addresses and block hashes pass through an ephemeral, zero-trust V8 sandbox managed by Vinkius. No data is cached or written to persistent disk, ensuring your blockchain queries remain isolated within your Google Cloud boundary.

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