How to Use the Blockchair (Universal Blockchain Search Engine & API) MCP in LlamaIndex

Q: How do I index transaction data from Blockchair (Universal Blockchain Search Engine & API) into LlamaIndex?

You use the McpToolSpec to load Blockchair tools like gettransaction and pass them to your agent. The LlamaIndex agent retrieves the transaction data and saves it as document nodes in your vector store.

Q: Can I run RAG queries on block data fetched by Blockchair (Universal Blockchain Search Engine & API) in LlamaIndex?

Yes, you can fetch Blockchair block details with getblock and index them. Your LlamaIndex pipeline then performs semantic search over the indexed block metadata to answer complex user queries.

Q: How do I handle address validation with Blockchair (Universal Blockchain Search Engine & API) inside LlamaIndex?

Your LlamaIndex agent runs the Blockchair validateaddress tool before indexing any wallet data. This ensures only valid blockchain addresses are processed and added to your LlamaIndex vector index.

Q: How do LlamaIndex agents retrieve ERC-20 balances dynamically?

The LlamaIndex agent uses geterc20tokenaddress to pull live token balances. LlamaIndex then injects this raw data into the current query context to ground the agent's response.

Index live multi-chain data directly into LlamaIndex vector stores for hallucination-free blockchain RAG using this MCP Server.

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Connect Blockchair (Universal Blockchain Search Engine & API) MCP to LlamaIndex

Create your Vinkius account to connect Blockchair (Universal Blockchain Search Engine & 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 Blockchair (Universal Blockchain Search Engine & API) with LlamaIndex

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Index multi-chain stats using this MCP Server

The `get_blockchain_stats` tool retrieves real-time difficulty, transaction counts, and fee metrics across multiple networks. LlamaIndex indexes these Blockchair statistics directly into your vector store, allowing your agent to query historical baselines without hallucinating numbers. By storing these live Blockchair metrics, your LlamaIndex RAG pipeline can compare current network congestion with past data. The index updates dynamically every time the LlamaIndex agent fetches fresh stats, keeping your local knowledge base accurate.

Convert transaction data into searchable LlamaIndex nodes

The `get_transaction` and `filter_transactions` tools fetch details for specific hashes and filter them based on customized criteria. Your LlamaIndex pipeline ingests these Blockchair transaction payloads via this MCP Server, parsing them into indexable document nodes for semantic search. When a user asks about a specific Blockchair payment pattern, the LlamaIndex agent queries the vector database instead of scanning the raw blockchain. This setup lets you search through complex Blockchair transaction histories using natural language.

Build a queryable index of ERC-20 balances

The `get_erc20_token_address` and `get_address` tools retrieve current token balances and historical address data. LlamaIndex structures this Blockchair data into a unified, queryable index, combining off-chain documents with live on-chain balances. Your LlamaIndex FunctionAgent can query this index to verify if an address holds enough assets before running a financial simulation. The integration ensures your agent relies on verified, indexed Blockchair facts rather than static training data.

Setup guide

Set up Blockchair (Universal Blockchain Search Engine & 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 Blockchair (Universal Blockchain Search Engine & 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 Blockchair (Universal Blockchain Search Engine & API) tools.",
)
response = await agent.run("List recent Blockchair (Universal Blockchain Search Engine & API) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Blockchair. 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 Blockchair (Universal Blockchain Search Engine & API) MCP in LlamaIndex

You use the McpToolSpec to load Blockchair tools like `get_transaction` and pass them to your agent. The LlamaIndex agent retrieves the transaction data and saves it as document nodes in your vector store.

Yes, you can fetch Blockchair block details with `get_block` and index them. Your LlamaIndex pipeline then performs semantic search over the indexed block metadata to answer complex user queries.

Your LlamaIndex agent runs the Blockchair `validate_address` tool before indexing any wallet data. This ensures only valid blockchain addresses are processed and added to your LlamaIndex vector index.

The LlamaIndex agent uses `get_erc20_token_address` to pull live token balances. LlamaIndex then injects this raw data into the current query context to ground the agent's response.

No, your transaction hashes and wallet addresses are processed locally within your LlamaIndex runtime. The Vinkius MCP gateway uses an ephemeral connection that does not cache or inspect your blockchain queries.

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