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

Q: What is the best way to research transactions using TzKT (Tezos Indexer & Explorer API) and CrewAI?

Assign a specialized 'Research Agent' that calls getoperationsbyhash. This lets your crew focus on specific transaction hashes, ensuring deep-dive investigation into Tezos activity.

Q: Can I list all types of assets using TzKT (Tezos Indexer & Explorer API) and CrewAI?

Yes. You can run a multi-step process: one agent calls listbigmaps, another uses getcontractstorage to check the data, giving you a complete picture of all indexed assets.

Q: How does CrewAI manage advanced filtering when listing blocks with TzKT (Tezos Indexer & Explorer API)?

The 'Coordinator Agent' manages the filters for tools like listblocks. You define criteria—like a date range or block level—and the agent executes the call to get exactly the required historical data.

Q: If I need account history, how do I use TzKT (Tezos Indexer & Explorer API) with CrewAI?

You set up a sequence where one agent calls getaccountbalancehistory and the next analyzes that resulting list to summarize trends or identify anomalies.

Run specialized Tezos analysis with CrewAI's multi-agent collaboration framework.

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Setup TzKT (Tezos Indexer & Explorer API) with CrewAI

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Autonomous Blockchain Research using MCP Server

You can set up a 'Researcher Agent' to run `list_transactions` and gather raw data. Then, an 'Analysis Agent' uses that input to call `get_account_report`, creating a summarized JSON report for the final 'Action Agent'. This makes complex Tezos auditing possible without manual steps.

MCP Server for Specialized Asset Tracking

The system can monitor multiple asset types. One agent uses `list_token_definitions` to get metadata, while another checks the actual movements using `list_token_transfers`. This parallel processing capability is perfect for investigating large-scale token dumps or migrations.

Multi-Agent Tezos Monitoring with CrewAI

Need to track staking activity? Assign a 'Staking Agent' to use `list_staking` and another 'Reward Agent' to check `get_delegator_rewards`. The agents collaborate, compiling all delegation data into one cohesive output for the user.

Setup guide

Set up TzKT (Tezos Indexer & Explorer API) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke TzKT (Tezos Indexer & Explorer API) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="TzKT (Tezos Indexer & Explorer API) Analyst",
    goal="Access and analyze TzKT (Tezos Indexer & Explorer API) data via MCP.",
    backstory="Expert analyst with direct TzKT (Tezos Indexer & Explorer API) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent TzKT (Tezos Indexer & Explorer API) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="TzKT (Tezos Indexer & Explorer API) Analyst",
        goal="Access and analyze TzKT (Tezos Indexer & Explorer API) data via MCP.",
        backstory="Expert analyst with direct TzKT (Tezos Indexer & Explorer API) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent TzKT (Tezos Indexer & Explorer API) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TzKT. 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 TzKT (Tezos Indexer & Explorer API) MCP in CrewAI

Assign a specialized 'Research Agent' that calls `get_operations_by_hash`. This lets your crew focus on specific transaction hashes, ensuring deep-dive investigation into Tezos activity.

Yes. You can run a multi-step process: one agent calls `list_bigmaps`, another uses `get_contract_storage` to check the data, giving you a complete picture of all indexed assets.

The server deals with public ledger information: account identifiers, token transfers, and contract code. The data is exclusively sourced from the Tezos blockchain record.

The 'Coordinator Agent' manages the filters for tools like `list_blocks`. You define criteria—like a date range or block level—and the agent executes the call to get exactly the required historical data.

You set up a sequence where one agent calls `get_account_balance_history` and the next analyzes that resulting list to summarize trends or identify anomalies.

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