How to Use the Mnemonic MCP in LlamaIndex

Q: How do I filter tools in LlamaIndex?

You use the allowedtools filter during setup. This limits the agent to only the specific Mnemonic functions you want it to access.

Index live NFT market data into LlamaIndex for semantic search and grounded asset intelligence.

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Connect Mnemonic MCP to LlamaIndex

Create your Vinkius account to connect Mnemonic 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 Mnemonic with LlamaIndex

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Semantic NFT indexing for LlamaIndex

Feed the output of `get_nft_details` and `get_contract_metadata` directly into your vector store. You create a knowledge base that tracks specific NFT attributes over time. This lets you query your index for 'collections with high distribution' using natural language. The agent retrieves the actual API data, ensuring your answers are based on the latest chain state.

Grounded market analysis with MCP Server

Stop guessing about floor prices and use `get_nft_prices` to populate your index. LlamaIndex combines this live data with your own documents for a unified view. When the agent retrieves info via `get_collection_distribution`, it's indexed alongside your internal research. This creates a searchable history of market conditions that you can reference anytime.

Unified asset retrieval and search

Use `list_collection_tokens` to pull broad datasets into your index for deeper analysis. The agent performs semantic searches across these tokens to find specific assets that match your criteria. You rely on the actual tool output to ground every response. By ignoring hallucinations, you ensure your RAG application provides precise, verifiable NFT market data.

Setup guide

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

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Common questions about Mnemonic MCP in LlamaIndex

You initialize the client and pass the tool spec to your agent. It maps the tools to your index, making them available for retrieval tasks.

Absolutely. Once the data is indexed, you can ask questions about collection trends or wallet holdings, and the agent pulls the relevant data from the server.

Yes, you can configure the server to return structured JSON that LlamaIndex parses easily. This makes it perfect for building a searchable library of NFT metadata.

You use the allowed_tools filter during setup. This limits the agent to only the specific Mnemonic functions you want it to access.

Your queries are processed locally by your agent. The server only sees the specific requests you send for NFT data, maintaining a clean boundary for your information.

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