How to Use the Moonriver (Moonriver Block Explorer API) MCP in LlamaIndex

Q: How do I load Moonriver (Moonriver Block Explorer API) MCP tools into my LlamaIndex pipeline?

Install llama-index-tools-mcp and set up the BasicMCPClient with your Vinkius endpoint. Convert the client to tools using McpToolSpec and pass them directly to your FunctionAgent.

Q: Can I index historical data from Moonriver (Moonriver Block Explorer API) into a vector database using LlamaIndex?

Yes, you can fetch transfer records using listtransfers and ingest them straight into a vector store. This lets your agent perform semantic searches over past transaction descriptions via MCP.

Q: Does the Moonriver (Moonriver Block Explorer API) MCP Server support filtering in LlamaIndex?

Yes, you can use the allowedtools filter when initializing your tool specification. This restricts your LlamaIndex agent to specific tools like getaccountinfo or getextrinsic.

Q: How does this help prevent LLM hallucinations about Moonriver chain state?

Your agent queries actual chain data via getmetadata and getextrinsic instead of guessing. The retrieved facts are injected directly into the LLM's context window.

Index Moonriver blockchain data directly into LlamaIndex vector stores for search without hallucinations.

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Connect Moonriver (Moonriver Block Explorer API) MCP to LlamaIndex

Create your Vinkius account to connect Moonriver (Moonriver Block 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 Moonriver (Moonriver Block Explorer API) with LlamaIndex

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Build searchable indexes from Moonriver account history

Use `get_account_info` to fetch real-time balance and nonce data, then index the results into your LlamaIndex document store. Your RAG pipeline searches this data to ground its answers in verified on-chain facts. This approach prevents your agent from hallucinating wallet balances or transaction counts. You get a reliable knowledge base that updates whenever your pipeline polls the server.

Parse EVM tokens with this LlamaIndex MCP Server

Use `list_evm_tokens` to pull the active token directory from Moonriver and feed it into your semantic search index. Your agent queries this local index to identify contract addresses and token symbols instantly. Combining live blockchain data with vector search allows your users to ask natural language questions about Moonriver assets. The agent retrieves the relevant token metadata from the index without scanning the entire chain every time.

Index block metadata for structural network analysis

Use `get_block` and `list_blocks` to retrieve block headers and timestamps, converting raw chain structural data into searchable nodes. LlamaIndex stores these nodes so your agent can quickly retrieve historical block patterns. This workflow bypasses the need for heavy local database infrastructure. You search through block histories using simple semantic queries, letting the framework handle the retrieval logic.

Setup guide

Set up Moonriver (Moonriver Block 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 Moonriver (Moonriver Block 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 Moonriver (Moonriver Block Explorer API) tools.",
)
response = await agent.run("List recent Moonriver (Moonriver Block Explorer API) 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 Moonriver. 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 Moonriver (Moonriver Block Explorer API) MCP in LlamaIndex

Install `llama-index-tools-mcp` and set up the `BasicMCPClient` with your Vinkius endpoint. Convert the client to tools using `McpToolSpec` and pass them directly to your `FunctionAgent`.

Yes, you can fetch transfer records using `list_transfers` and ingest them straight into a vector store. This lets your agent perform semantic searches over past transaction descriptions via MCP.

Yes, you can use the `allowed_tools` filter when initializing your tool specification. This restricts your LlamaIndex agent to specific tools like `get_account_info` or `get_extrinsic`.

Your agent queries actual chain data via `get_metadata` and `get_extrinsic` instead of guessing. The retrieved facts are injected directly into the LLM's context window.

All API requests pass through an encrypted, zero-trust connection managed by Vinkius. No query payloads, block numbers, or token lists are cached or exposed to external networks by this MCP configuration.

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