How to Use the Givebutter MCP in Pydantic AI

Q: How do I verify my Givebutter connection in Pydantic AI?

You can have your agent run verifyapiconnection right after initialization. This ensures your API token is valid before you execute any data pipelines.

Q: Can Pydantic AI handle Givebutter recurring plans?

Yes, the agent calls listrecurringplans to fetch subscription details. The response is parsed into clean Python objects for easy validation.

Q: What happens if a Givebutter webhook payload changes in Pydantic AI?

If you query listdonationwebhooks and the schema doesn't match your models, the framework raises a validation error. This alerts you to API changes before they break your production code.

Q: Can I log offline checks using this setup?

Yes, your agent can call recordofflinedonation to register manual payments. The input arguments are strictly validated before the call is sent.

Build type-safe fundraising workflows in Pydantic AI with this Givebutter MCP Server.

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Connect Givebutter MCP to Pydantic AI

Create your Vinkius account to connect Givebutter to Pydantic 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 Givebutter with Pydantic AI

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Validate Givebutter schemas at runtime with Pydantic AI

Prevent corrupted Givebutter donor profiles by enforcing strict type checks in Pydantic AI. When your agent calls `list_donor_contacts` or `sync_donor_contact` when using this MCP Server, the data is validated against strict Pydantic models. If Givebutter returns unexpected fields or missing data, your application raises an immediate validation error. This keeps your internal database clean and prevents silent data corruption.

Track transactions with strict type safety

Ensure your financial reporting is accurate by validating incoming transactions. The agent queries `list_donation_transactions` and `get_transaction_details` to verify every dollar received. Because every field is strictly typed, your agent won't misinterpret string amounts as integers. This guarantees that your automated accounting scripts parse transaction metadata correctly every time.

Monitor campaign performance safely

Query active fundraisers using `list_fundraising_campaigns` and `get_campaign_details` without worrying about API drift. The agent parses campaign metadata into structured, type-safe Python objects. This MCP setup removes the guesswork from agent actions. Your agent gets a clear, validated schema of your fundraising progress, making it easy to generate reliable executive summaries.

Setup guide

Set up Givebutter MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "givebutter-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Givebutter tools.",
)

result = await agent.run("List recent Givebutter transactions")
print(result.output)

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Common questions about Givebutter MCP in Pydantic AI

You can have your agent run `verify_api_connection` right after initialization. This ensures your API token is valid before you execute any data pipelines.

Yes, the agent calls `list_recurring_plans` to fetch subscription details. The response is parsed into clean Python objects for easy validation.

If you query `list_donation_webhooks` and the schema doesn't match your models, the framework raises a validation error. This alerts you to API changes before they break your production code.

Yes, your agent can call `record_offline_donation` to register manual payments. The input arguments are strictly validated before the call is sent.

Your Givebutter campaign metadata and donor profiles are processed locally in your secure Pydantic AI runtime. The MCP connection uses HTTPS encryption, ensuring no sensitive data is exposed during transit.

Use it with your favorite AI tools

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

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