How to Use the Classy.org MCP in LlamaIndex

Q: How do I index Classy.org data using LlamaIndex?

Install llama-index-tools-mcp and instantiate the BasicMCPClient. Wrap it in McpToolSpec to convert the Classy.org endpoints into MCP tools, which you can then use to load data directly into your LlamaIndex documents.

Q: Can LlamaIndex search through old Classy.org donation transactions?

Yes. By calling listdonationtransactions through the MCP Server, LlamaIndex can pull historical transaction records, index them into a vector store, and let your agent perform semantic queries over past donor contributions.

Q: How do I limit which Classy.org MCP tools LlamaIndex can access?

You can use the allowedtools filter when initializing the client in LlamaIndex. This lets you restrict your agent to only read campaign data via listfundraisingcampaigns while blocking access to sensitive member lists.

Index live Classy.org fundraising data directly into LlamaIndex vector stores for ground-truth RAG applications.

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Connect Classy.org MCP to LlamaIndex

Create your Vinkius account to connect Classy.org 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 Classy.org with LlamaIndex

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Build RAG indexes on your Classy.org campaigns

The Classy.org MCP Server lets LlamaIndex query `get_campaign_details` and `list_fundraising_campaigns` to build real-time vector indexes. Your agent queries this active index to answer questions with actual, current numbers. By feeding live data from Classy.org straight into your vector store, you eliminate the gap between your dashboard and your agent. The model no longer hallucinates campaign progress because it reads directly from your active fundraising metrics.

Search your Classy.org donor base semantically

Using `list_classy_members` with LlamaIndex lets you fetch your supporter list and index it for semantic search. Now your agent can find donors interested in specific causes based on past participation notes. The tool `get_activity_feed` feeds recent interactions into the same index. This keeps your agent updated on who just registered or donated, allowing for highly contextual semantic queries during live chat sessions.

Analyze Classy.org peer-to-peer pages via LlamaIndex

This server exposes `list_individual_fundraising_pages` and `list_fundraising_teams` so LlamaIndex can parse and structure peer-to-peer campaigns. Peer-to-peer fundraising generates massive amounts of distributed data across hundreds of individual pages, but indexing makes it manageable. It converts the raw JSON output from these Classy.org endpoints into queryable index nodes. You can ask your LlamaIndex agent which individual pages are closest to their goals, and it will return precise, verified answers.

Setup guide

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

Install `llama-index-tools-mcp` and instantiate the `BasicMCPClient`. Wrap it in `McpToolSpec` to convert the Classy.org endpoints into MCP tools, which you can then use to load data directly into your LlamaIndex documents.

Yes. By calling `list_donation_transactions` through the MCP Server, LlamaIndex can pull historical transaction records, index them into a vector store, and let your agent perform semantic queries over past donor contributions.

Only if you configure your index to store them. By default, the LlamaIndex agent queries the Classy.org endpoints like `get_campaign_details` dynamically, ensuring you get the most recent fundraising figures during your RAG runs.

You can use the `allowed_tools` filter when initializing the client in LlamaIndex. This lets you restrict your agent to only read campaign data via `list_fundraising_campaigns` while blocking access to sensitive member lists.

Vinkius secures all API calls using zero-trust, ephemeral V8 isolates. Your Classy.org donor lists and campaign structures are processed in memory and never stored on Vinkius servers, maintaining strict compliance with donor privacy standards.

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