How to Use the Lineascan MCP in LlamaIndex

Q: How do I index Lineascan data within LlamaIndex?

Use the McpToolSpec to register the server tools. Once the client is initialized, you can pass the output to your vector store.

Q: Can I query Lineascan history using LlamaIndex?

Yes. You can run tokentx or txlistinternal to pull deep history and add it to your knowledge base for semantic search.

Q: Does Lineascan work with LlamaIndex agents?

It integrates directly into the FunctionAgent. You just call the tool list and let the agent decide when to fetch fresh data.

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Connect Lineascan MCP to LlamaIndex

Create your Vinkius account to connect Lineascan 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 Lineascan with LlamaIndex

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Claude

Perplexity

Convert Lineascan output into knowledge

LlamaIndex turns the result of your `get_logs` or `tx_list` calls into searchable vectors. Your agent doesn't just read the data; it catalogs it for future queries. You ask questions about past transactions and get answers grounded in the actual data stored in your index.

Ground AI answers with Lineascan data

Stop worrying about model hallucinations. You feed the output of `eth_get_block_by_number` directly into your knowledge base for accurate, time-stamped context. Your RAG application uses these tools to pull facts that the model hasn't seen before. The index stays updated with the latest network state.

Filter Lineascan tools for your index

You control which tools are available to the agent using the allowed_tools filter. You restrict access to read-only functions like `get_balance` or `eth_supply`. This keeps your index clean and focused. You only ingest the data points that matter for your specific research or tracking task.

Setup guide

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

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Common questions about Lineascan MCP in LlamaIndex

Use the `McpToolSpec` to register the server tools. Once the client is initialized, you can pass the output to your vector store.

Yes. You can run `token_tx` or `tx_list_internal` to pull deep history and add it to your knowledge base for semantic search.

It integrates directly into the `FunctionAgent`. You just call the tool list and let the agent decide when to fetch fresh data.

Vinkius manages all auth via your endpoint token. The communication is ephemeral and scoped only to your active agent session.

We treat transaction hashes and addresses as transient payloads. These sensitive identifiers move through the encrypted Vinkius sandbox and are never cached.

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