How to Use the Bloomerang MCP in CrewAI

Deploy specialized CrewAI agent teams to manage Bloomerang donors, track campaigns, and coordinate fundraising tasks.

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Connect Bloomerang MCP to CrewAI

Create your Vinkius account to connect Bloomerang to CrewAI 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 CrewAI

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Multi-agent fundraising coordination in CrewAI

The `list_constituents` tool provides your specialized CrewAI researcher agent with the raw Bloomerang donor data needed to analyze giving histories. This agent works alongside a coordinator agent to systematically evaluate donor engagement. Because CrewAI agents share memory, the coordinator agent knows exactly what the researcher found. The coordinator then runs `list_tasks` to check outstanding actions for those specific Bloomerang profiles.

Autonomous transaction auditing via MCP Server

The `list_transactions` tool lets an autonomous CrewAI auditing crew monitor your incoming Bloomerang donations in real time using this MCP setup. A specialized auditor agent parses the transaction feed and flags high-value gifts. Once a major gift is identified, the auditor agent passes the ID to a writer agent. The writer agent uses `get_transaction` to pull the exact details and drafts a personalized thank-you letter for the Bloomerang donor.

Selective tool exposure for specialized agents

The `list_campaigns` tool lets you expose high-level Bloomerang fundraising progress to your CrewAI agents while keeping sensitive donor profiles restricted. By using CrewAI's tool filtering, you control exactly which agents can see financial data. This means your marketing agents can read `list_funds` and `list_appeals` to draft promotional copy, while tools like `create_constituent` remain locked down for administrative agents only.

Setup guide

Set up Bloomerang MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Bloomerang tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Bloomerang Analyst",
    goal="Access and analyze Bloomerang data via MCP.",
    backstory="Expert analyst with direct Bloomerang access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Bloomerang transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Bloomerang Analyst",
        goal="Access and analyze Bloomerang data via MCP.",
        backstory="Expert analyst with direct Bloomerang access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Bloomerang transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

The easiest way is passing your endpoint URL directly into the mcps list when defining your agent. This automatically configures the agent to use tools like list_constituents.

Yes, your agents can coordinate task management autonomously. An agent can check outstanding items with list_tasks and then decide which donor profiles need immediate follow-up.

Yes, CrewAI supports SSE, stdio, and Streamable HTTP transports. When using MCPServerHTTP, the framework handles the connection details so your agents can query list_appeals without issues.

CrewAI agents use shared memory to pass details. Once an agent calls get_constituent, the retrieved donor profile becomes part of the crew's collective context for subsequent tasks.

All donor profiles, contact info, and notes accessed via get_constituent or list_notes are processed locally within your execution environment. Vinkius acts as a zero-trust MCP bridge, ensuring no sensitive donor information is stored.

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