How to Use the Infura (Ethereum Node RPC Provider) MCP in LlamaIndex

Index live blockchain data from Infura (Ethereum Node RPC Provider) directly into your LlamaIndex knowledge base.

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Connect Infura (Ethereum Node RPC Provider) MCP to LlamaIndex

Create your Vinkius account to connect Infura (Ethereum Node RPC Provider) to LlamaIndex 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 Infura (Ethereum Node RPC Provider) with LlamaIndex

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Index live chain state with LlamaIndex

Turn your RPC tool outputs into searchable knowledge. Use `eth_getLogs` or `eth_getBlockByNumber` to fetch data, then index it into your vector store for later retrieval. This approach builds a historical record of blockchain events that your agent can query using natural language. It prevents hallucinations by grounding every answer in the data you retrieved from the network.

Query Infura (Ethereum Node RPC Provider) intelligently

LlamaIndex handles the heavy lifting of context management for your blockchain data. When you call `eth_call`, the result is saved and becomes part of the agent's queryable memory. This creates a RAG-ready environment where live API data meets your local documents. Your agent answers questions about contract states or past transactions by searching its own internal index.

Advanced MCP Server tool integration

Convert all 20 RPC methods into a structured tool list for your FunctionAgent. The integration maps every tool schema automatically, ensuring your agent knows exactly how to format `eth_estimateGas` requests. This setup allows you to filter which tools are accessible to the agent. You keep tight control over what your agent can do on the Ethereum mainnet while maintaining full visibility into its actions.

Setup guide

Set up Infura (Ethereum Node RPC Provider) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Infura (Ethereum Node RPC Provider) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Infura (Ethereum Node RPC Provider) tools.",
)
response = await agent.run("List recent Infura (Ethereum Node RPC Provider) data")

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Common questions about Infura (Ethereum Node RPC Provider) MCP in LlamaIndex

Use the MCP client to fetch tools and wrap them in a ToolSpec. This allows your LlamaIndex agent to discover and use RPC methods as native functions.

Indexing allows your agent to compare current state with historical logs. It turns raw RPC responses into a permanent knowledge base for complex analytical tasks.

The data is stored in your local vector index. Access is restricted to the agent logic you define, keeping your blockchain queries isolated from external exposure.

LlamaIndex manages the request cadence. You can configure your agent to batch queries or use an internal cache to reduce the frequency of redundant RPC calls.

Absolutely. You can fetch logs or state data, store them in your index, and then prompt the agent to synthesize answers from that stored blockchain information.

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