How to Use the Nansen (Blockchain Analytics) MCP in LlamaIndex

Q: How do I connect LlamaIndex to Nansen (Blockchain Analytics)?

Use the llama-index-tools-mcp library to instantiate a client pointed at your Vinkius endpoint. Once configured, you convert the tools to a list and pass them into your FunctionAgent for immediate use.

Index live on-chain market data into your LlamaIndex knowledge base using Nansen (Blockchain Analytics).

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Connect Nansen (Blockchain Analytics) MCP to LlamaIndex

Create your Vinkius account to connect Nansen (Blockchain Analytics) 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 Nansen (Blockchain Analytics) with LlamaIndex

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Index live market intelligence

Turn real-time API responses into searchable vector embeddings. By piping `tgm_flow_intelligence` results directly into your LlamaIndex pipeline, you create a knowledge base that tracks current market sentiment rather than static documents. This allows your RAG application to answer questions about recent whale activity using actual on-chain data. The agent doesn't hallucinate; it queries the indexed state of the blockchain.

Query historical wallet behavior

Ground your AI responses in historical data retrieved via `profiler_historical_balances`. LlamaIndex handles the ingestion of these snapshots, making it easy to perform semantic searches over a wallet's past holdings. You no longer need to manually parse large JSON payloads. Just index the retrieved data and let the agent perform natural language queries against the wallet's track record.

Build RAG apps with Nansen (Blockchain Analytics)

Combine your private documents with live Nansen (Blockchain Analytics) insights. You can use the `McpToolSpec` to pull metadata from `tgm_token_information` and augment your existing RAG context window. This approach gives your agent a complete view of the market. It understands both your internal research and the objective truth visible on the chain.

Setup guide

Set up Nansen (Blockchain Analytics) 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 Nansen (Blockchain Analytics) 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 Nansen (Blockchain Analytics) tools.",
)
response = await agent.run("List recent Nansen (Blockchain Analytics) 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 Nansen. 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 Nansen (Blockchain Analytics) MCP in LlamaIndex

Use the `llama-index-tools-mcp` library to instantiate a client pointed at your Vinkius endpoint. Once configured, you convert the tools to a list and pass them into your FunctionAgent for immediate use.

Yes, by providing fresh, verifiable data from the chain. Your agent will reference the output of `prediction_market_screener` instead of relying on outdated training data.

Once you index the tool outputs, they become searchable in your vector store. You can query your previous research sessions to find patterns in wallet behavior without re-running the same API calls.

You can use LlamaIndex's built-in filtering to limit the data ingested. Focus on specific endpoints like `profiler_pnl_summary` to keep your vector store lean and performant.

Your data is processed in an ephemeral Vinkius sandbox. We only ever touch the specific wallet or token metrics requested, and your indexed knowledge base remains entirely under your local control.

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