How to Use the TzKT (Tezos Indexer & Explorer API) MCP in LlamaIndex

Q: How does LlamaIndex manage complex queries using TzKT (Tezos Indexer & Explorer API)?

LlamaIndex indexes the results of tools like listtransactions. When you ask a question, it retrieves relevant chunks from this knowledge base first. Then, it uses those indexed facts to guide the live API calls for precise answers.

Q: Can LlamaIndex cross-reference contract and account data using TzKT (Tezos Indexer & Explorer API)?

Yes. You index getcontractcode outputs alongside getaccountreport outputs. This lets your agent answer contextual questions, such as 'Does this contract code relate to the activity seen in this account report?'

Q: Is LlamaIndex effective for tracking token movements with TzKT (Tezos Indexer & Explorer API)?

It’s great. By indexing listtokentransfers and related metadata, your agent doesn't just list transfers; it understands the context of those transfers based on your historical data.

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

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Connect TzKT (Tezos Indexer & Explorer API) MCP to LlamaIndex

Create your Vinkius account to connect TzKT (Tezos Indexer & 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 TzKT (Tezos Indexer & Explorer API) with LlamaIndex

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Search historical account activity.

You run `get_account_report` for an account, and instead of just seeing the JSON, you index that full report. Later, when asking a question about 'last quarter's spending,' LlamaIndex searches the knowledge base using the indexed data. This means your agent can answer questions grounded in actual API outputs—like finding out why certain transactions occurred—without having to re-query the live server every time.

Query smart contract metadata.

LlamaIndex lets you index results from `list_contracts` and `get_contract_code`. You can build a knowledge base that answers questions like, 'What was the initial purpose of this specific contract?' When querying, LlamaIndex combines semantic search against these indexed documents with live data retrieved via other tools like `get_contract_entrypoints`, giving you context-rich answers.

Build a unified view of token movement.

You index results from `list_token_transfers` and `list_bigmaps`. This creates a searchable map of asset flow. If you ask, 'What was the biggest single transfer involving this bigmap last month?' LlamaIndex queries both the indexed data and live transfers. This capability allows developers to combine document retrieval (from past API calls) with real-time `list_token_balances` checks.

Setup guide

Set up TzKT (Tezos Indexer & 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 TzKT (Tezos Indexer & 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 TzKT (Tezos Indexer & Explorer API) tools.",
)
response = await agent.run("List recent TzKT (Tezos Indexer & Explorer API) data")

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Common questions about TzKT (Tezos Indexer & Explorer API) MCP in LlamaIndex

LlamaIndex indexes the results of tools like `list_transactions`. When you ask a question, it retrieves relevant chunks from this knowledge base first. Then, it uses those indexed facts to guide the live API calls for precise answers.

Yes. You index `get_contract_code` outputs alongside `get_account_report` outputs. This lets your agent answer contextual questions, such as 'Does this contract code relate to the activity seen in this account report?'

It’s great. By indexing `list_token_transfers` and related metadata, your agent doesn't just list transfers; it understands the context of those transfers based on your historical data.

Because you index both live and past results, the answers are grounded in actual blockchain data. You get an answer derived from real `list_blocks` or `get_head` outputs, not general knowledge.

This server touches on diverse structured data types: account activity reports, contract storage contents, block metadata, and token transfer records (`list_token_transfers`).

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