How to Use the Donorbox MCP in LangChain

Build multi-step fundraising pipelines in LangChain by chaining Donorbox tools for automated campaign and donor analysis.

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Connect Donorbox MCP to LangChain

Create your Vinkius account to connect Donorbox to LangChain 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 Donorbox with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain fundraising data in LangChain

Feed the output of `list_fundraising_campaigns` directly into your reasoning chains. Your agent evaluates campaign health by passing campaign IDs into `get_campaign_details`. You build logic where the agent decides the next step based on live data. It pulls the latest figures to decide if a follow-up action is needed for specific campaigns.

Automate donor communication flows

Connect `list_registered_donors` to your existing messaging tools. Your agent identifies high-value donors and fetches their history using `search_donors_by_email`. This setup creates a feedback loop where the agent updates internal records. It processes donor lists and triggers custom workflows based on individual contribution patterns.

Track recurring revenue automatically

Monitor subscription growth by calling `list_recurring_donation_plans` within your LangChain agent. Your agent flags plans that need attention by comparing them against `list_active_subscription_plans`. You gain clear visibility into subscription health without manual checks. The agent reconciles active plans against total donations to provide an accurate picture of monthly revenue.

Setup guide

Set up Donorbox MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Donorbox tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "donorbox-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Donorbox transactions"
    })
    print(result["messages"][-1].content)

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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 Donorbox. 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.

Why Choose Vinkius

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Common questions about Donorbox MCP in LangChain

Use the MCP adapter to connect the server to your LangChain environment. Once connected, your agent calls the tools directly within its reasoning loop.

Yes, but keep an eye on your API limits. Structure your agent to request only the necessary data segments to avoid hitting the ceiling.

The server remains stateless, but your LangChain agent can store results in its own memory. This lets you build a history of donor interactions over time.

You can use standard tracing to log every tool input and output. This shows you exactly how the agent is querying your donation data.

The MCP Server only exposes the data you explicitly query. Your API keys remain local, and the server processes donor records only when the agent requests them.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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