How to Use the Nodereal MCP in LlamaIndex

Q: How do I use the Nodereal MCP server with LlamaIndex?

Install llama-index-tools-mcp and set up a BasicMCPClient. Wrap it with McpToolSpec and call totoollistasync(). Feed those tools into your FunctionAgent.

Q: Can LlamaIndex search the results of Nodereal queries?

That is the main advantage. When your agent calls nrgetnftinventory, LlamaIndex can embed the JSON response. You then perform semantic searches over the wallet's actual holdings.

Q: How do I filter which MCP tools my agent can see?

Use the allowedtools parameter when initializing the tool spec. You might only want your agent to read data, so you restrict it to ethgetbalance and block ethsendrawtransaction.

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Connect Nodereal MCP to LlamaIndex

Create your Vinkius account to connect Nodereal 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 Nodereal with LlamaIndex

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Ground RAG Applications in On-Chain State

`aptos_get_ledger_info` and `eth_get_logs` allow this MCP Server to feed raw blockchain data straight into your LlamaIndex vector stores. Your application doesn't just read the chain. It indexes the responses. When a user asks about network activity, the query engine searches the indexed tool outputs instead of hallucinating. You can set up a routine that pulls `nr_get_token_holders` every hour and updates your semantic index. Now your RAG pipeline knows exactly who holds what token, grounding its answers in verifiable, real-time NodeReal infrastructure.

Vectorize Smart Contract Resources

`aptos_get_account_modules` grabs the actual Move bytecode and module definitions for any Aptos account. Your LlamaIndex agent can chunk this code, embed it, and store it. Developers can then query their knowledge base to understand how a specific contract operates. Combine this with `aptos_get_account_resources` to index the current state alongside the code. If someone asks why a transaction failed, the agent searches the indexed state variables and cross-references them with the module logic.

Query Historical EVM MCP Server Data

`eth_get_transaction_receipt` and `debug_trace_transaction` pull deep historical execution data for your indexing pipelines. Standard LLMs have no idea what happened in block 14,000,000. By calling these tools, your agent pulls the exact trace and adds it to the document store. This turns LlamaIndex into a highly specific block explorer. Users ask natural language questions about gas usage or internal calls, and the system retrieves the exact trace data from the underlying NodeReal endpoints to formulate the answer.

Setup guide

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

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Common questions about Nodereal MCP in LlamaIndex

Install `llama-index-tools-mcp` and set up a `BasicMCPClient`. Wrap it with `McpToolSpec` and call `to_tool_list_async()`. Feed those tools into your `FunctionAgent`.

That is the main advantage. When your agent calls `nr_get_nft_inventory`, LlamaIndex can embed the JSON response. You then perform semantic searches over the wallet's actual holdings.

Yes. You get eight dedicated Aptos tools. You can pull block data using `aptos_get_blocks_by_height` and feed the transaction lists straight into your document nodes.

Use the `allowed_tools` parameter when initializing the tool spec. You might only want your agent to read data, so you restrict it to `eth_get_balance` and block `eth_send_raw_transaction`.

The tools process raw wallet addresses and contract hashes to fetch balances and logs. Vinkius enforces a zero-trust, ephemeral architecture, ensuring that your inputs are strictly routed to the endpoint without being logged or cached in transit.

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