How to Use the Bloomerang MCP in LlamaIndex

Q: How do I index Bloomerang data inside LlamaIndex?

You map the tools to your agent and set the includeresources flag to true. The system then pulls and indexes your campaign and constituent data automatically.

Q: Can I filter Bloomerang data for LlamaIndex?

You use the allowedtools filter to limit the agent's access. This restricts which parts of your CRM the agent can index and query.

Index your Bloomerang donor history into LlamaIndex for semantic searching.

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Connect Bloomerang MCP to LlamaIndex

Create your Vinkius account to connect Bloomerang 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 Bloomerang with LlamaIndex

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Index Bloomerang records with LlamaIndex

This MCP Server converts your donor database into a searchable vector index. LlamaIndex pulls data from `list_constituents` and stores it for future semantic queries. Querying your fundraising history becomes a natural language task. Instead of manual lookups, you ask your agent to find donors who match specific giving patterns.

Ground LlamaIndex answers in Bloomerang data

Your RAG application uses real-time API data to prevent hallucinations. The agent references `list_transactions` to answer questions about specific donation dates or amounts. Everything retrieved becomes part of your unified knowledge base. You combine donor notes with your internal documents to get a complete picture of your fundraising health.

Search Bloomerang via LlamaIndex tools

The McpToolSpec bridges the gap between your API and your agent's index. You filter results to include only what matters for your current analysis. Updating your index requires a simple trigger. Once the agent calls `list_funds`, the new data is indexed and immediately available for your next query.

Setup guide

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

You map the tools to your agent and set the include_resources flag to true. The system then pulls and indexes your campaign and constituent data automatically.

Yes, the server provides access to all transaction records. Once retrieved, these entries are embedded into your vector store for instant retrieval.

The framework manages the index state based on your configuration. You control how often the agent re-fetches data from your fundraising lists.

You use the allowed_tools filter to limit the agent's access. This restricts which parts of your CRM the agent can index and query.

Data is scoped to your specific agent session and never shared between other instances. We enforce strict access controls on every `get_constituent` request to keep individual donor identities isolated.

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