How to Use the Arweave MCP in Google ADK

Q: How do I initialize the Arweave MCP Server in Google ADK?

Import McpToolset from the SDK and configure it with your Vinkius HTTP endpoint. Pass the toolset object to your LlmAgent to instantly expose tools like getnetworkinfo and gettransaction.

Q: How do I restrict which Arweave tools my Google ADK agent can access?

You can use the toolnames filter when initializing your McpToolset. This lets you restrict your agent to read-only operations like getwalletbalance while blocking write tools like submittransaction.

Q: How do I calculate transaction costs before writing data?

Your agent can call getstorageprice with the size of your payload in bytes. The tool returns the cost in Winstons, allowing the agent to verify you have enough funds via getwalletbalance.

Q: How does the Google ADK integration secure my Arweave transaction payloads?

All raw payloads retrieved via gettransactiondata flow directly through Vinkius's ephemeral V8 sandboxes. No data is cached on our servers, ensuring your business-critical blockchain data remains isolated within your Google Cloud boundary.

Connect Gemini enterprise agents to the Arweave permaweb using Google ADK to query and write permanent data alongside BigQuery pipelines.

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Connect Arweave MCP to Google ADK

Create your Vinkius account to connect Arweave 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 Arweave with Google ADK

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Long-context blockchain analysis with Google ADK

The Arweave MCP Server equips your Gemini-powered enterprise agents to analyze massive blocks of immutable data. Your agent can fetch large transaction payloads using `get_transaction_data` and feed them directly into Gemini's million-token context window. This setup lets you run deep historical analysis on decentralized logs. Your agents can parse years of transaction histories retrieved via `get_block_by_height` without running out of context memory or losing track of details.

Enterprise data archiving via this MCP Server

This MCP Server allows your Google Cloud agent pipelines to archive database records permanently. Your Python agents can pull old records from BigQuery and write them to the permaweb using `submit_transaction`. Before writing, the agent calls `get_storage_price` to estimate the cost in Winstons. This ensures your enterprise cloud budget remains predictable, as you only pay a one-time fee to store transaction logs forever.

Query decentralized datasets via GraphQL

The Arweave MCP Server exposes structured querying capabilities directly to your Vertex AI workflows. Your agents can execute complex queries using `query_graphql` to filter transaction metadata based on custom tags. This eliminates the need to build a custom indexer. The agent directly queries the network, extracts the relevant transaction IDs, and fetches the underlying data using `get_transaction_offset` for processing.

Setup guide

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

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Common questions about Arweave MCP in Google ADK

Import `McpToolset` from the SDK and configure it with your Vinkius HTTP endpoint. Pass the toolset object to your `LlmAgent` to instantly expose tools like `get_network_info` and `get_transaction`.

Yes, Gemini's large context window allows the agent to process massive payloads. The agent calls `get_transaction_data` to retrieve the files and loads them directly into its context for reasoning.

You can use the `tool_names` filter when initializing your `McpToolset`. This lets you restrict your agent to read-only operations like `get_wallet_balance` while blocking write tools like `submit_transaction`.

Your agent can call `get_storage_price` with the size of your payload in bytes. The tool returns the cost in Winstons, allowing the agent to verify you have enough funds via `get_wallet_balance`.

All raw payloads retrieved via `get_transaction_data` flow directly through Vinkius's ephemeral V8 sandboxes. No data is cached on our servers, ensuring your business-critical blockchain data remains isolated within your Google Cloud boundary.

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