How to Use the BlockPi (Distributed RPC Network) MCP in CrewAI

Deploy autonomous agent crews on CrewAI that interact with BlockPi (Distributed RPC Network) nodes.

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Connect BlockPi (Distributed RPC Network) MCP to CrewAI

Create your Vinkius account to connect BlockPi (Distributed RPC Network) 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 BlockPi (Distributed RPC Network) with CrewAI

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Perplexity

Specialized agents for BlockPi (Distributed RPC Network)

Assign a dedicated monitor agent to run `get_ru_consumed` and report back to your primary analyst agent. This role-based setup ensures that cost tracking happens separate from your main execution logic. Your agents collaborate using shared memory to keep track of RU usage. They decide when it is time to switch endpoints based on the data provided by these tools.

Execute complex JSON-RPC chains

Use `rpc_call` to fetch chain states that inform your crew's decision-making process. One agent retrieves the data, and another interprets the results to perform specific actions. This sequential execution allows for sophisticated automated operations. Your agents handle the heavy lifting while you define the strategy for how they interact with the network.

Automated wallet management for crews

Task your administrative agent with calling `get_wallet_balance` regularly. It ensures your operational budget is always sufficient for the tasks currently assigned to the crew. If the balance is insufficient, the agent can pause the crew's activity to prevent failure. It keeps your autonomous systems running smoothly without requiring a human to check the dashboard.

Setup guide

Set up BlockPi (Distributed RPC Network) 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 BlockPi (Distributed RPC Network) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="BlockPi (Distributed RPC Network) Analyst",
    goal="Access and analyze BlockPi (Distributed RPC Network) data via MCP.",
    backstory="Expert analyst with direct BlockPi (Distributed RPC Network) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent BlockPi (Distributed RPC Network) 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="BlockPi (Distributed RPC Network) Analyst",
        goal="Access and analyze BlockPi (Distributed RPC Network) data via MCP.",
        backstory="Expert analyst with direct BlockPi (Distributed RPC Network) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent BlockPi (Distributed RPC Network) 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 BlockPi. 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 BlockPi (Distributed RPC Network) MCP in CrewAI

You define the server in your agent's configuration. Each agent can then invoke the specific tools they need, such as `rpc_call`, to complete their assigned tasks.

Yes, they use the framework's shared memory to exchange RPC results. One agent can fetch the block number, and the next agent uses that data to perform a deeper analysis.

It is. You can assign the server to a manager agent that delegates specific RPC tasks to its subordinates. This keeps your tool usage organized and efficient.

You use the tool filter in your MCP setup. This restricts which agents can call `rpc_call` or check balances, enforcing strict operational boundaries within your crew.

All communication between your agents and the server happens over a secure, authenticated channel. We do not store any of the metadata or RPC payloads processed by your agents.

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