How to Use the Bloomerang MCP in Mastra AI

Build automated workflows that update Bloomerang donors and track campaigns using Mastra AI agents.

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Connect Bloomerang MCP to Mastra AI

Create your Vinkius account to connect Bloomerang to Mastra AI 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 Mastra AI

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Automated donor onboarding with Mastra AI

The `create_constituent` tool lets your Mastra AI agent automatically build Bloomerang donor profiles when a supporter signs up. The framework's workflow engine coordinates the entire setup, ensuring new constituents are registered without manual data entry. If the initial API call to Bloomerang fails due to a network hiccup, Mastra's built-in retry engine handles it. The agent will attempt the profile write again using exponential backoff before triggering any failure paths.

Conditional fundraising follow-ups via MCP Server

The `get_transaction` tool allows your Mastra AI agent to pull financial records and build complex branching logic based on Bloomerang donor behavior. If a transaction pulled through this MCP integration exceeds a specific value, the workflow branches to flag the donor for high-priority outreach. The agent then uses `list_tasks` to check if a team member is already assigned to this Bloomerang constituent. If the list is empty, Mastra schedules a new task automatically, keeping your major donor pipeline moving.

Human-in-the-loop approvals for financial updates

The `list_notes` and `list_tasks` tools allow you to require human approval in Mastra AI before your agent modifies critical Bloomerang record histories. This ensures that automated follow-up logs are verified by a teammate before going live. When the agent attempts to write a task or update notes, Mastra AI pauses the workflow and waits for authorization. Once approved, the tool executes and writes the updates directly to your Bloomerang database.

Setup guide

Set up Bloomerang MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Bloomerang tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "bloomerang-mcp-client",
  servers: {
    "bloomerang-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Bloomerang Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Bloomerang tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Bloomerang transactions"
);
console.log(result.text);

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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 Mastra AI

Initialize the client using new MCPClient and point it to your endpoint. Then, call mcpClient.listTools() and spread those tools directly into your agent configuration array.

Yes, Mastra's workflow engine manages rate limits gracefully. When running list_constituents or list_transactions, the framework automatically retries failed requests if the API throttles your connection.

The framework automatically detects the correct transport protocol, whether it is SSE or Streamable HTTP. You only need to provide your endpoint URL during initialization to start querying list_campaigns.

Yes, you can set up a polling workflow in Mastra. The agent calls list_funds and list_campaigns to check progress, then executes your custom notification logic when goals are reached.

Absolutely, all transaction details and constituent notes accessed via get_transaction or list_notes travel through TLS-encrypted channels. Vinkius runs this MCP Server in a secure sandbox, ensuring your financial data is never cached or exposed.

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