How to Use the Alchemy MCP in LlamaIndex

Q: Can I filter which Alchemy tools LlamaIndex sees?

Yes, you can use the allowedtools filter during setup. This lets you restrict the agent to only the specific data points you want to index.

Index live blockchain data into LlamaIndex. Ground your RAG apps in real-time Ethereum network state.

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Connect Alchemy MCP to LlamaIndex

Create your Vinkius account to connect Alchemy 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 Alchemy with LlamaIndex

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Index wallet data with LlamaIndex

Convert `get_token_balances` output into searchable vectors. LlamaIndex takes this live API data and stores it for semantic retrieval later. This turns your agent into an expert on specific Ethereum addresses. Your RAG app queries historical balances without needing manual updates.

Query block history in LlamaIndex

Use `get_latest_block_number` to feed your knowledge base with current network status. LlamaIndex indexes these snapshots to provide context for user queries. Your system stays grounded in reality. The agent searches the index to answer questions about network activity based on the latest data.

Add transaction context to LlamaIndex

Feed `get_transaction_receipt` results into your vector store. LlamaIndex maps these receipts to help your agent explain specific contract interactions. Your knowledge base grows with every call. It provides the source of truth for your agent when it answers complex questions about transaction outcomes.

Setup guide

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

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Common questions about Alchemy MCP in LlamaIndex

Yes. LlamaIndex takes the tool output and adds it to your index, making the API data searchable alongside your documents.

Use the BasicMCPClient to connect, then pass the McpToolSpec to your FunctionAgent. This makes the tools available for semantic search and retrieval.

Yes, you can use the allowed_tools filter during setup. This lets you restrict the agent to only the specific data points you want to index.

You can configure your RAG pipeline to trigger tool calls whenever a user query requires fresh data. The system then indexes that new result.

The server only reads public on-chain information. Your local index stores these public records, and no sensitive credentials are ever transmitted.

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