How to Use the DappRadar (Web3 Dapp & NFT Analytics) MCP in LlamaIndex

Q: How do I add DappRadar (Web3 Dapp & NFT Analytics) to LlamaIndex?

Run pip install llama-index-tools-mcp and set up a BasicMCPClient. Wrap the client in McpToolSpec and pass the asynchronous tool list to your agent.

Q: Does the MCP Server support the includeresources flag?

It works perfectly with includeresources=True if you need raw data access. The agent reads the market statistics and adds them to the query context automatically.

Q: How do I filter which blockchains LlamaIndex can query?

Use the allowedtools parameter when initializing your agent. You can restrict the framework to only use the chain listing tool if you want to prevent it from pulling NFT data.

Index live Web3 market data from DappRadar directly into your LlamaIndex vector stores for ground-truth semantic search.

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Connect DappRadar (Web3 Dapp & NFT Analytics) MCP to LlamaIndex

Create your Vinkius account to connect DappRadar (Web3 Dapp & NFT 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 DappRadar (Web3 Dapp & NFT Analytics) with LlamaIndex

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Vectorize Blockchain Statistics

The `get_chain_stats` tool feeds active wallet counts and transaction volumes straight into your LlamaIndex knowledge base. You query a specific network, and the MCP integration returns the raw metrics as structured text. Your RAG application takes that output and embeds it alongside your internal research documents. When a user asks about layer-1 growth, the system searches the index and answers using the actual API data pulled from `list_chains` rather than guessing.

Ground RAG Apps in DappRadar Data

Pulling historical protocol activity requires the `get_dapp_metrics` and `get_dapp` tools. The agent requests specific decentralized applications, receiving smart contract interactions and user counts over time. You configure your `FunctionAgent` to run `list_dapps` when it needs to discover new protocols in a specific category. The resulting list gets vectorized, allowing your users to semantically search for trending decentralized exchanges based on live statistics.

Semantic Search for NFT Collections via MCP Server

Your LlamaIndex setup uses `get_nft_collection` to extract floor prices and metadata for specific digital assets. The tool returns current market statistics that the framework instantly converts into searchable nodes. To map broader market trends, the agent calls `list_nft_collections` and `get_nft_collection_metrics`. It indexes the historical trader volume, so when you ask which profile-picture projects maintained volume last month, the answer comes directly from the embedded Web3 data.

Setup guide

Set up DappRadar (Web3 Dapp & NFT 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 DappRadar (Web3 Dapp & NFT 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 DappRadar (Web3 Dapp & NFT Analytics) tools.",
)
response = await agent.run("List recent DappRadar (Web3 Dapp & NFT 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 DappRadar. 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 DappRadar (Web3 Dapp & NFT Analytics) MCP in LlamaIndex

Run `pip install llama-index-tools-mcp` and set up a `BasicMCPClient`. Wrap the client in `McpToolSpec` and pass the asynchronous tool list to your agent.

Yes, the framework treats the returned JSON as document text. You can split and embed the historical activity metrics to build a searchable database of protocol performance.

It works perfectly with `include_resources=True` if you need raw data access. The agent reads the market statistics and adds them to the query context automatically.

Use the `allowed_tools` parameter when initializing your agent. You can restrict the framework to only use the chain listing tool if you want to prevent it from pulling NFT data.

We run every connection through an ephemeral, zero-trust environment. When your LlamaIndex application requests smart contract interaction logs, the sandbox processes the API call and immediately wipes the memory state.

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