How to Use the Salsa Engage MCP in LlamaIndex

Q: How can LlamaIndex help me understand my Salsa Engage segments?

You can build an agent that periodically calls listsupportersegments and listsupporters to index your audience data. Then, you can ask your LlamaIndex app questions like, 'Show me supporters in the Major Donors segment who haven't donated this year.'

Q: What's the best way to use LlamaIndex and Salsa Engage to analyze campaign results?

Your agent can use getengagementmetrics to fetch the raw numbers. LlamaIndex excels at then indexing this data over time, so you can ask comparative questions like, 'How did the year-end appeal's click-through rate compare to last year's?'

Q: What happens to my supporter PII when I query it with LlamaIndex through this Salsa Engage MCP?

Supporter data, including profiles accessed via listsupporters, is processed in a zero-trust sandbox for each request. Vinkius manages the connection, but the data itself is only held in memory for the duration of the tool call and is not persisted.

Turn your Salsa Engage data into a searchable knowledge base with LlamaIndex. Ask questions, get answers from your own supporter activity.

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Connect Salsa Engage MCP to LlamaIndex

Create your Vinkius account to connect Salsa Engage to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Salsa Engage with LlamaIndex

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Index Your Supporter Activity

Use the `McpToolSpec` to pull data from Salsa Engage. LlamaIndex can call `list_supporters` and `list_engagement_activities`, then index the results into a vector store. Now your supporter history is a queryable asset. This creates a living knowledge base. When you ask, 'Who were our most active supporters in Q2?', your agent doesn't guess. It runs a semantic search against the indexed data from those tool calls to give you a grounded answer.

Query Your Fundraising Performance

Your agent can run `get_engagement_metrics` and `list_offline_donations` and index the output. This lets you ask complex questions in plain English, like 'Compare email open rates for the spring fundraiser against offline donations for the same period.' LlamaIndex combines live API calls with indexed historical data. It can fetch fresh metrics with `get_engagement_metrics` and compare them to past performance already in its knowledge base, giving you context that a single API call can't provide.

Build RAG Apps on Your Salsa Engage MCP Server

This is about more than just calling tools. It's about building applications that reason over your non-profit's data. A RAG agent can use `list_supporter_segments` to understand your audiences and then use that context to answer strategic questions. You can build an internal chatbot for your team. When a manager asks, 'What groups exist for volunteers?', the agent queries its index built from `list_supporter_groups` to provide an instant, accurate list. This MCP server acts as the live data source for your knowledge base.

Setup guide

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

You can build an agent that periodically calls `list_supporter_segments` and `list_supporters` to index your audience data. Then, you can ask your LlamaIndex app questions like, 'Show me supporters in the Major Donors segment who haven't donated this year.'

Yes, it can call tools that change data. Based on a prompt, the agent can use `upsert_supporter_profile` to add a new contact or update an existing one's information.

Your agent can use `get_engagement_metrics` to fetch the raw numbers. LlamaIndex excels at then indexing this data over time, so you can ask comparative questions like, 'How did the year-end appeal's click-through rate compare to last year's?'

LlamaIndex lets you ask questions across different data types. You can correlate data from `list_engagement_activities` with `list_offline_donations` in a single query, something that's hard to do in a standard UI.

Supporter data, including profiles accessed via `list_supporters`, is processed in a zero-trust sandbox for each request. Vinkius manages the connection, but the data itself is only held in memory for the duration of the tool call and is not persisted.

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