How to Use the Galxe MCP in LlamaIndex

Q: How do I handle large leaderboard payloads in index queries?

The tool returns structured JSON from getspaceleaderboard. LlamaIndex chunks this data automatically, allowing your agent to run semantic queries over hundreds of rows of participant data.

Index live Web3 community data into LlamaIndex using the Galxe MCP Server for real-time RAG applications.

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Connect Galxe MCP to LlamaIndex

Create your Vinkius account to connect Galxe 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 Galxe with LlamaIndex

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Claude

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Build a Web3 knowledge base with LlamaIndex

Instead of guessing user status, let your LlamaIndex agent query the MCP Server to pull live quest data. Use `get_quest` to fetch campaign details and index them directly into your vector store. This turns raw Web3 campaign structures into a searchable knowledge base. Your agent can answer complex user queries about campaign requirements using verified, real-time data instead of relying on outdated documents.

Ground RAG answers in live Galxe leaderboards

Keep your community dashboards accurate by indexing leaderboard data. Use `get_space_leaderboard` to ingest loyalty points directly into your LlamaIndex query engine. By combining this with Twitter metrics from `get_starboard_leaderboard`, your RAG application can instantly answer who is leading the current campaign. The agent queries the index to serve fresh, grounded answers.

Context-aware credential verification

Let your LlamaIndex agent check user access during active chat sessions. By calling `check_credential_eligibility`, the agent retrieves the exact status of a user's Web3 credentials. If they aren't eligible, the agent uses `check_quest_eligibility` to find out what they missed. It then searches your indexed campaign docs to guide the user on how to qualify.

Setup guide

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

You load the tools via the LlamaIndex tool spec and use them to fetch raw text from `get_quest`. LlamaIndex converts this JSON output into document nodes for semantic vector search.

Yes, you can connect LlamaIndex to your community chat and use `check_quest_eligibility` to answer why a user cannot claim a badge. The bot checks their wallet live and explains the missing steps.

You pass an allowed tools list to the constructor to restrict access. For example, you can expose `get_space_leaderboard` while keeping `mutate_credential_items` locked down.

The tool returns structured JSON from `get_space_leaderboard`. LlamaIndex chunks this data automatically, allowing your agent to run semantic queries over hundreds of rows of participant data.

Absolutely. This MCP Server only handles public wallet addresses and public Twitter handles retrieved via `get_starboard_leaderboard`. Vinkius runs the server in an ephemeral container, so no user identifiers are permanently stored or exposed.

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