How to Use the Wemix Explorer MCP in LlamaIndex

Q: Can I search past transaction records using Wemix Explorer with LlamaIndex?

Yes. You run listaccounttransfers and then index the output. Later, you can query that indexed data for specific patterns or timelines.

Q: What if I want to track token changes over a long period using Wemix Explorer and LlamaIndex?

You run the necessary listing tools like listaccounttokens, then chunk and index that data. This allows semantic search across months or years of transactions.

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Connect Wemix Explorer MCP to LlamaIndex

Create your Vinkius account to connect Wemix Explorer 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 Wemix Explorer with LlamaIndex

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Indexing past token flows for semantic search.

Don't just query; build a searchable knowledge base from the data. You can run `list_account_transfers` and then index those results into your vector store. Later, you ask a question like 'Where did assets go in Q3?' and get answers grounded in that historical API data. This is critical because LlamaIndex turns structured blockchain outputs into searchable knowledge.

Combining document context with live data.

LlamaIndex lets you combine a PDF manual with live API results. For instance, you upload the project governance rules and then query it alongside the current state from `get_account_info`. The answer is cross-referenced against both documents. Your RAG application gets answers that are both authoritative (from docs) and current (from the MCP Server).

Querying deep block details across sessions.

You can use `get_block` to fetch a specific historical block. By indexing this data, you'll never forget what was inside it. Later queries can ask about the contents of that block without needing the original ID. This capability makes your index robust for deep dives into network history.

Setup guide

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

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Common questions about Wemix Explorer MCP in LlamaIndex

It provides the live API data that LlamaIndex needs to build its knowledge graph. The AI client calls tools like `list_tokens`, and those outputs become searchable vectors in your index.

Yes. You run `list_account_transfers` and then index the output. Later, you can query that indexed data for specific patterns or timelines.

It's designed to enrich your knowledge base. You combine external documents (like PDFs) with live API calls from `get_event` into one unified, queryable index.

You run the necessary listing tools like `list_account_tokens`, then chunk and index that data. This allows semantic search across months or years of transactions.

This server handles account addresses, token identifiers, block metadata, and transfer details—all used to build your searchable knowledge base.

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