How to Use the Conflux MCP in LlamaIndex

Q: How do I connect the Conflux MCP Server to LlamaIndex?

First, run pip install llama-index-tools-mcp to get the required MCP integration. Then, initialize BasicMCPClient with your Vinkius endpoint, convert it with McpToolSpec, and pass the resulting tool list to your FunctionAgent.

Q: Can LlamaIndex index contract logs from Conflux?

Yes, you can use cfxgetlogs or ethgetlogs to fetch events, then parse and index those logs into a vector store. This lets your RAG application perform semantic searches over contract events.

Q: How do I limit which Conflux tools LlamaIndex can access?

You can use the allowedtools filter when setting up your McpToolSpec. This lets you restrict your agent to read-only tools like cfxgetbalance while blocking transaction-sending capabilities.

Index live Conflux blockchain data into your LlamaIndex vector store using this MCP Server.

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Connect Conflux MCP to LlamaIndex

Create your Vinkius account to connect Conflux 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 Conflux with LlamaIndex

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Index live network states into vector stores using LlamaIndex

The Conflux MCP Server provides tools like `cfx_get_status` and `cfx_get_account` to query the live state of the blockchain network. LlamaIndex reads these outputs and indexes them as document nodes, allowing you to search past ledger states semantically. This setup prevents your agent from hallucinating account balances or network conditions. Instead of guessing, your RAG pipeline queries the newly indexed blockchain data to ground its answers in verifiable on-chain facts.

Build a searchable transaction history

Retrieving transaction histories relies on `cfx_get_transaction_by_hash` and `eth_get_transaction_receipt` to fetch precise transaction details. Your LlamaIndex agent can fetch these records, convert them into text chunks, and store them in a local vector index. You can then ask natural language questions about past transfers or contract interactions. The system searches the indexed logs fetched by `cfx_get_logs` or `eth_get_logs` to find the exact transaction you are looking for.

Query epoch and block data with semantic search

Accessing block information is done through `cfx_get_block_by_epoch_number` and `eth_get_block_by_number` to pull structural data from the ledger. LlamaIndex ingests these block details, making it easy to cross-reference transactions across different epochs. You can build applications that analyze network congestion or gas trends over time. The agent queries the index of block data to give you detailed reports on gas prices and contract activity.

Setup guide

Set up Conflux 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 Conflux 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 Conflux tools.",
)
response = await agent.run("List recent Conflux 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 Conflux. 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 Conflux MCP in LlamaIndex

First, run `pip install llama-index-tools-mcp` to get the required MCP integration. Then, initialize `BasicMCPClient` with your Vinkius endpoint, convert it with `McpToolSpec`, and pass the resulting tool list to your `FunctionAgent`.

Yes, you can use `cfx_get_logs` or `eth_get_logs` to fetch events, then parse and index those logs into a vector store. This lets your RAG application perform semantic searches over contract events.

You can use the `allowed_tools` filter when setting up your `McpToolSpec`. This lets you restrict your agent to read-only tools like `cfx_get_balance` while blocking transaction-sending capabilities.

Yes, the server includes tools for both environments. Your agent can query Core Space using tools like `cfx_get_best_block_hash` and eSpace using `eth_get_block_by_number` within the same LlamaIndex application.

Absolutely. The server only reads public data like wallet addresses, transaction receipts, and block hashes. Vinkius hosts the server in a secure, ephemeral V8 isolate sandbox, ensuring no private transaction data is ever logged or exposed.

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