How to Use the Nearblocks (Near Blockchain Explorer API) MCP in LlamaIndex

Q: How do I use the Nearblocks (Near Blockchain Explorer API) tools in my LlamaIndex app?

Install llama-index-tools-mcp and create a BasicMCPClient with your server URL. Wrap it in the McpToolSpec and call totoollistasync() to get the tools. You can then pass this list to your agent.

Q: Can I choose which Nearblocks tools my LlamaIndex agent can use?

Yes. The McpToolSpec constructor accepts an allowedtools argument. You can pass a list of tool names, like 'getaccountdetails', 'gettransactiondetails', to restrict the agent's capabilities.

Q: How does indexing work with data like getrecenttransactions?

When your agent calls getrecenttransactions, LlamaIndex takes the list of transactions returned by the MCP tool and chunks it into nodes. Each node is vectorized and stored, making the content of those transactions searchable for future queries.

Index Near blockchain data into a searchable knowledge base with LlamaIndex.

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Connect Nearblocks (Near Blockchain Explorer API) MCP to LlamaIndex

Create your Vinkius account to connect Nearblocks (Near Blockchain 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 Nearblocks (Near Blockchain Explorer API) with LlamaIndex

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Perplexity

Turn Blockchain Data into Q&A

Stop just fetching data; start indexing it. With LlamaIndex, the output from tools like `get_account_transactions` and `get_account_inventory` isn't just a one-time result. It's automatically indexed into a vector store, creating a persistent, searchable knowledge base of on-chain activity. This means you can ask questions in plain English. Instead of manually calling `get_transaction_details` with a specific hash, you can ask your agent, "What was the gas fee for my last transaction to foundation.near?" LlamaIndex finds the relevant indexed data and synthesizes an answer.

Build RAG on Live Network State

This is how you ground your AI in reality. Use the Nearblocks tools to build a Retrieval-Augmented Generation (RAG) system that answers questions using up-to-the-minute blockchain data. Your agent can combine static documents with live results from `get_network_stats` or `get_latest_blocks`. For example, you could index your project's technical documentation alongside the live output of `get_token_details` for your project's token. When a user asks about tokenomics, the agent can pull from both the static docs and the live on-chain data for a complete, accurate answer.

Query Your On-Chain History

Create an index of your personal or your project's entire on-chain history. By periodically running and indexing tools like `get_account_tokens` and `get_account_transactions`, you build a semantic search engine for your own activity. It's like a personal block explorer. This lets you run complex, stateful queries that a simple MCP server call can't handle. You could ask, "Show me all the NFTs I acquired in May" or "List all tokens I've held that are no longer in my wallet." LlamaIndex compares the indexed snapshots to figure out the answer.

Setup guide

Set up Nearblocks (Near Blockchain 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 Nearblocks (Near Blockchain 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 Nearblocks (Near Blockchain Explorer API) tools.",
)
response = await agent.run("List recent Nearblocks (Near Blockchain Explorer API) data")

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Common questions about Nearblocks (Near Blockchain Explorer API) MCP in LlamaIndex

Install `llama-index-tools-mcp` and create a `BasicMCPClient` with your server URL. Wrap it in the `McpToolSpec` and call `to_tool_list_async()` to get the tools. You can then pass this list to your agent.

Yes. The `McpToolSpec` constructor accepts an `allowed_tools` argument. You can pass a list of tool names, like `['get_account_details', 'get_transaction_details']`, to restrict the agent's capabilities.

The key difference is indexing. LlamaIndex doesn't just use the tool output once; it stores it in a vector index. This lets you build RAG applications that answer natural language questions about historical on-chain data from the Near protocol.

When your agent calls `get_recent_transactions`, LlamaIndex takes the list of transactions returned by the MCP tool and chunks it into nodes. Each node is vectorized and stored, making the content of those transactions searchable for future queries.

The MCP server itself is stateless and only touches public Near blockchain data like account balances. When you use LlamaIndex, you are responsible for the vector index where this data is stored. You control the security and privacy of that indexed knowledge base.

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