How to Use the FtmScan (Fantom Network Explorer) MCP in LangChain

Q: How do I add the FtmScan (Fantom Network Explorer) MCP Server to my LangChain agent?

Install the adapter with pip install langchain-mcp-adapters langgraph and initialize the MultiServerMCPClient pointing to the Vinkius endpoint. Call client.gettools() to retrieve the 19 tools and pass them directly to your agent constructor.

Q: Can I trace internal smart contract calls using FtmScan (Fantom Network Explorer) inside LangChain?

Yes, you use getinternaltransactions to track execution steps that do not appear on the main transaction ledger. Your agent can chain this output with gettransactionreceiptstatus to verify if a complex call succeeded.

Q: Can my agent check gas fees and block rewards?

Yes, your agent can call getblockreward to inspect block details or getftmlastprice to calculate transaction costs in fiat currency. This allows your agent to make smart decisions about when to submit transactions.

Run multi-step Fantom data pipelines by hooking this MCP server directly into your LangChain chains.

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Connect FtmScan (Fantom Network Explorer) MCP to LangChain

Create your Vinkius account to connect FtmScan (Fantom Network Explorer) to LangChain 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 FtmScan (Fantom Network Explorer) with LangChain

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ChatGPT

Claude

Perplexity

Trace Fantom transactions in chains

`get_normal_transactions` pulls the transfer history of any wallet address on the Fantom network. Your agent evaluates these incoming transactions, extracts the recipient addresses, and pipes them directly into the next step of your chain. You do not need to write custom parsing code or glue APIs together manually. By using this MCP tool inside a ReAct loop, your agent inspects ledger data on the fly and decides whether to trigger deeper analysis.

Analyze smart contracts via LangChain

`get_contract_source_code` retrieves the raw Solidity code of verified contracts. Your LangChain agent reads this source code to verify function names and check security patterns before executing transactions. LangSmith traces every step of this process, showing you the exact code retrieved and how your agent interpreted it. You can combine this with `get_contract_abi` to build dynamic transaction payloads based on live on-chain definitions.

Monitor Fantom token balances in real time

`get_erc20_token_balance` checks the exact holdings of any address for a given token contract. Your agent aggregates these balances to build real-time financial profiles of target wallets. With LangChain's multi-server client, you can mix these token checks with external database queries. You get a clean flow where on-chain data feeds directly into your analytical systems without manual intervention.

Setup guide

Set up FtmScan (Fantom Network Explorer) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes FtmScan (Fantom Network Explorer) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "ftmscan-fantom-network-explorer-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent FtmScan (Fantom Network Explorer) transactions"
    })
    print(result["messages"][-1].content)

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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 FtmScan. 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 FtmScan (Fantom Network Explorer) MCP in LangChain

Install the adapter with `pip install langchain-mcp-adapters langgraph` and initialize the `MultiServerMCPClient` pointing to the Vinkius endpoint. Call `client.get_tools()` to retrieve the 19 tools and pass them directly to your agent constructor.

Yes, you use `get_internal_transactions` to track execution steps that do not appear on the main transaction ledger. Your agent can chain this output with `get_transaction_receipt_status` to verify if a complex call succeeded.

The MCP server runs in a managed Vinkius sandbox that handles connection pooling. You can use LangSmith to monitor latency and token usage for every tool call like `get_logs` or `get_ftm_balance` in your pipeline.

Yes, your agent can call `get_block_reward` to inspect block details or `get_ftm_last_price` to calculate transaction costs in fiat currency. This allows your agent to make smart decisions about when to submit transactions.

Vinkius hosts this server in an isolated, zero-trust V8 sandbox. Your queries for wallet addresses, transaction hashes, and contract ABIs go through an encrypted connection, and we never log your private search parameters.

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