How to Use the Donately MCP in LlamaIndex

Index your Donately records in LlamaIndex to build RAG apps that ground answers in real donor history and campaign performance.

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Connect Donately MCP to LlamaIndex

Create your Vinkius account to connect Donately 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 Donately with LlamaIndex

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Ground LlamaIndex in donor data

Index the output of `list_all_donations` into a vector store to make your fundraising data searchable. Your RAG app can now answer questions about specific contribution patterns. Instead of searching manually, you ask your agent to find trends. It pulls from the index you populated with the MCP Server and provides context-aware summaries.

Query campaigns via LlamaIndex

Feed `get_campaign_performance_summary` results into your knowledge base for instant recall. You no longer have to hunt through spreadsheets to understand how a specific campaign performed. Your agent builds a semantic map of your fundraising efforts. When you ask about past performance, the system retrieves the actual data points from your indexed Donately records.

Searchable donor profiles in LlamaIndex

Use `get_donor_profile` to pull deep insights into your knowledge index. Your agent can compare donation history across your entire database to find commonalities. This turns static records into a living knowledge base. You can query your donor base using natural language, and the system finds the relevant information via the MCP Server tools.

Setup guide

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

You use the MCP client to fetch data and store it in your vector index. Once the data is in the index, your queries run against that ground truth.

Yes. After you index your donations, you can perform semantic searches. The agent finds the most relevant donor or campaign records based on your input.

The server uses specific API tokens to pull your data. Your indexed knowledge base remains private and only accessible to the agents you configure.

You can re-run your indexing tool periodically to pull fresh data. This keeps your RAG application current with the latest contributions and donor profiles.

Sensitive donor email addresses are indexed as part of the profile data. The system treats this information as private, ensuring it is only used for your defined agent tasks.

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