How to Use the GivingFuel MCP in LlamaIndex

Q: Can I filter GivingFuel results before indexing in LlamaIndex?

Yes, use the filtering tools like filterordersbystatus to narrow down the data before it hits your index. This keeps your vector store clean and reduces token overhead.

Index your GivingFuel data into LlamaIndex to create queryable knowledge bases grounded in live donation records.

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Connect GivingFuel MCP to LlamaIndex

Create your Vinkius account to connect GivingFuel 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 GivingFuel with LlamaIndex

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Index GivingFuel data for semantic search

Transform the output of `list_crm_contacts` into searchable vector embeddings. By doing this, you allow your agent to answer questions about donor history using facts from your own database. This turns static API results into a living knowledge base. Instead of querying raw endpoints, you ask natural language questions that pull context directly from your actual donation records.

Combine docs and GivingFuel API data

Merge your internal documents with live results from `list_donation_registrants`. You can now query across both data sources to understand how specific donors interact with your fundraising materials. This unified index approach ensures your agent doesn't hallucinate. It relies on the data provided by the MCP Server to ground every single response it gives.

Ground AI answers with live MCP Server data

Use `list_financial_transactions` to inject real-time updates into your RAG pipeline. This keeps your index fresh without needing to manually refresh your data store every hour. Your agent queries the live API only when necessary, keeping your knowledge base current. It’s the fastest way to ensure your AI has the most accurate view of your campaign performance.

Setup guide

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

It is. You can use the tool spec to import all available endpoints as functions. Once registered, these tools provide the live data needed to build your searchable index.

You wrap the tools in a spec and pass them to your agent. The agent then calls the relevant endpoints, and you index the resulting output into your vector store.

You can store donor profiles, transaction history, and campaign metrics. Any JSON output from the tools can be indexed as a document for your RAG system.

Your API data stays within your local index and memory. The MCP Server acts only as a bridge, and no information is stored on Vinkius infrastructure beyond the connection itself.

Yes, use the filtering tools like `filter_orders_by_status` to narrow down the data before it hits your index. This keeps your vector store clean and reduces token overhead.

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