How to Use the Aurorascan (Aurora Network L2 Block Explorer API) MCP in CrewAI

Q: How do I configure Aurorascan (Aurora Network L2 Block Explorer API) tools for a CrewAI agent?

You pass the MCP Server URL directly into the agent's mcps list during initialization. This instantly grants that specific agent access to tools like getbalance and getlogs while keeping other agents focused on different tasks.

Run autonomous multi-agent teams that monitor, audit, and track Aurora L2 blockchain activity using CrewAI and this MCP Server.

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Connect Aurorascan (Aurora Network L2 Block Explorer API) MCP to CrewAI

Create your Vinkius account to connect Aurorascan (Aurora Network L2 Block Explorer API) to CrewAI 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 Aurorascan (Aurora Network L2 Block Explorer API) with CrewAI

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Collaborative Aurora Smart Contract Audits

Set up a dedicated crew of agents using this MCP Server where each has a specific job. One agent fetches verified code using `get_source_code`, while another analyzes the ABI retrieved via `get_abi`. A third agent can then run gas simulations using `proxy_estimate_gas` to verify execution efficiency. CrewAI manages the handoffs between these agents automatically. They share memory and context, meaning the auditing agent knows exactly what the fetching agent found without you writing glue code.

Autonomous Wallet and Token Monitoring Teams

Deploy a team of agents that continuously watch Aurora addresses. Your monitoring agent queries `get_tx_list` and `get_token_tx` to track incoming and outgoing transfers. If it spots a suspicious transaction, it passes the hash to an escalation agent. The escalation agent then uses `proxy_get_transaction_receipt` to check the execution status and gathers details on the involved tokens. You get a fully autonomous security desk running 24/7.

Hierarchical Chain Analysis and Reporting

Use a hierarchical CrewAI structure to generate deep reports on Aurora network activity. A manager agent delegates tasks to specialized sub-agents, directing them to fetch stats like `get_eth_supply` or `get_eth_price`. The sub-agents gather the data from this MCP Server and pass it back up. Your manager agent compiles the findings into a clean summary, giving you an accurate picture of L2 network health without manual intervention.

Setup guide

Set up Aurorascan (Aurora Network L2 Block Explorer API) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Aurorascan (Aurora Network L2 Block Explorer API) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Aurorascan (Aurora Network L2 Block Explorer API) Analyst",
    goal="Access and analyze Aurorascan (Aurora Network L2 Block Explorer API) data via MCP.",
    backstory="Expert analyst with direct Aurorascan (Aurora Network L2 Block Explorer API) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Aurorascan (Aurora Network L2 Block Explorer API) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Aurorascan (Aurora Network L2 Block Explorer API) Analyst",
        goal="Access and analyze Aurorascan (Aurora Network L2 Block Explorer API) data via MCP.",
        backstory="Expert analyst with direct Aurorascan (Aurora Network L2 Block Explorer API) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Aurorascan (Aurora Network L2 Block Explorer API) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Aurorascan. 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 Aurorascan (Aurora Network L2 Block Explorer API) MCP in CrewAI

You pass the MCP Server URL directly into the agent's `mcps` list during initialization. This instantly grants that specific agent access to tools like `get_balance` and `get_logs` while keeping other agents focused on different tasks.

Yes, CrewAI's shared memory allows agents to pass findings to each other. If your researcher agent pulls a transaction list using `get_tx_list`, your analyst agent can immediately inspect those transactions without making redundant API calls.

You can use CrewAI's tool filters to limit which agents have access to specific endpoints. This prevents your agents from spamming `get_token_balance` simultaneously and hitting the block explorer's rate limits.

CrewAI supports stdio, SSE, and Streamable HTTP transports. For most production environments running multi-agent teams, connecting via Streamable HTTP provides the most stable connection for continuous background queries.

Your wallet addresses, contract code, and transaction logs are processed locally or within your private cloud. The MCP Server acts as a secure bridge, passing data directly to your agents without storing or caching any sensitive blockchain information.

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