How to Use the IoTeX (IoTeX Block Explorer API) MCP in LlamaIndex

Q: Can I index transaction histories for multiple accounts simultaneously?

Yes, you can loop through multiple addresses and call getaccountactions for each. Your LlamaIndex pipeline packages these transaction logs into separate document objects, which are then indexed into your vector store for cross-account analysis.

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Connect IoTeX (IoTeX Block Explorer API) MCP to LlamaIndex

Create your Vinkius account to connect IoTeX (IoTeX Block Explorer 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 IoTeX (IoTeX Block Explorer API) with LlamaIndex

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The `get_block_by_height` and `get_block_by_hash` tools provide the raw block data needed to feed your document indexes via the MCP. Your LlamaIndex pipeline ingests these block structures, turning transaction metadata into searchable nodes that ground your agent's answers in on-chain reality. You don't have to worry about your model guessing block details. By indexing the exact payload returned from the ledger, your application answers questions using verified, structured block history instead of training weights.

Query token supply metrics dynamically

The `get_token` and `get_token_holders` tools retrieve live supply figures and address distributions for any token on the network. LlamaIndex uses these tools to pull real-time data, converting the raw JSON into vector node embeddings on the fly. This enables semantic search over historical token distributions. Your agent queries the index to identify holding patterns and track changes in wallet concentrations over time.

Retrieve structured transaction histories

The `get_account_actions` and `get_recent_actions` tools fetch historical transaction lists for any target address. Your pipeline indexes these actions, allowing your agent to search past transactions by gas spent, action type, or timestamp. This removes the need for custom indexing databases. You get a direct bridge from the IoTeX explorer to your LlamaIndex query engine using this MCP setup, keeping your knowledge base updated with every transaction.

Setup guide

Set up IoTeX (IoTeX Block Explorer 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 IoTeX (IoTeX Block Explorer 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 IoTeX (IoTeX Block Explorer API) tools.",
)
response = await agent.run("List recent IoTeX (IoTeX Block Explorer API) data")

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Common questions about IoTeX (IoTeX Block Explorer API) MCP in LlamaIndex

It queries the API tools to fetch actual blockchain records before generating text. By pulling real-time data via `get_account` or `get_action`, the engine inserts verified facts directly into the prompt context, preventing the model from inventing transaction details.

Yes, you can loop through multiple addresses and call `get_account_actions` for each. Your LlamaIndex pipeline packages these transaction logs into separate document objects, which are then indexed into your vector store for cross-account analysis.

The tools return raw structured data, which LlamaIndex then embeds for semantic search. You use `get_token_holders` or `get_recent_actions` to gather data, and the framework handles the embedding generation so you can search the blockchain records using natural language.

You initialize the MCP client with the Vinkius endpoint and convert it to a tool spec. Pass this spec to your LlamaIndex agent, allowing it to call `get_account` or `get_block_by_height` dynamically whenever a query demands blockchain state.

The server only reads public blockchain records like account balances, block numbers, and transaction hashes. No private keys are ever requested or stored. Vinkius isolates each connection in a secure V8 sandbox, preventing data leaks and keeping your local index queries completely segregated.

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