How to Use the Blocknative (Ethereum Mempool API) MCP in LlamaIndex

Index live Ethereum gas data into your LlamaIndex knowledge base for grounded reasoning.

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Connect Blocknative (Ethereum Mempool API) MCP to LlamaIndex

Create your Vinkius account to connect Blocknative (Ethereum Mempool API) 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 Blocknative (Ethereum Mempool API) with LlamaIndex

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Vectorizing gas data for LlamaIndex

Turn ephemeral gas prices into searchable knowledge. By calling `get_gas_prices`, you store past market states in your index for future analysis. Your RAG application compares historical gas trends against current spikes. This grounds your agent's answers in specific data rather than guesses.

Context-aware Ethereum analysis

Retrieve past gas performance to inform current LlamaIndex queries. The agent looks back at how the mempool behaved during similar network events. This creates a feedback loop where your index grows smarter about Ethereum congestion over time. You stop relying on static thresholds.

Unified API and document search

Combine your private documentation with live mempool data from this MCP Server. LlamaIndex treats the results of `get_gas_prices` as just another document in your store. Your agent answers user questions about transaction costs by referencing both your internal manuals and current blockchain reality. It makes your knowledge base live.

Setup guide

Set up Blocknative (Ethereum Mempool API) 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 Blocknative (Ethereum Mempool API) 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 Blocknative (Ethereum Mempool API) tools.",
)
response = await agent.run("List recent Blocknative (Ethereum Mempool API) data")

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Common questions about Blocknative (Ethereum Mempool API) MCP in LlamaIndex

You can store the output of the gas tools into your index. This allows the LlamaIndex agent to retrieve that data later for comparison.

The tool output gets converted into text nodes. These nodes are then embedded into your vector store for retrieval.

The data is only as persistent as your vector store. Once the agent indexes the gas prices, they remain queryable until you delete them.

Your agent receives a standard error message. You can handle this in your code to retry or fail gracefully.

The server only reads public gas prices. Your indexed history is stored locally in your chosen vector database, keeping your data within your infrastructure.

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