How to Use the Double the Donation MCP in Pydantic AI

Q: How do I add Double the Donation to a Pydantic AI agent?

Install the dependency using pip install "pydantic-ai-slimmcp" and initialize MCPToolset with your server's HTTP endpoint. Pass this toolset into the toolsets parameter of your Agent to instantly expose the MCP Server.

Q: Does Double the Donation support checking subsidiary relationships with Pydantic AI?

Yes, the agent uses listsubsidiaries to trace corporate hierarchies when a donor names a local branch. This ensures the matching rules of the parent company are correctly mapped and validated.

Q: Can I retrieve individual donation details safely?

You call getdonation to fetch specific transaction records for validation. The agent parses the response into typed Python models, ensuring absolute structural correctness.

Validate corporate match registrations at runtime with Pydantic AI and this MCP Server to eliminate bad donation data.

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Connect Double the Donation MCP to Pydantic AI

Create your Vinkius account to connect Double the Donation 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 Double the Donation with Pydantic AI

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Validate donor registration payloads with Pydantic AI

The `register_donation` tool records new transactions in the database to trigger employer match requests. Your Pydantic AI agent validates the payload structure against strict types before sending the request. This prevents broken API calls from corrupting your tracking data. If a donor provides an invalid email format or empty name, the agent catches it instantly at runtime.

Look up company records using this type-safe MCP Server

The `search_company` tool searches the corporate database to find matching gift programs that fit the donor's employer. Your agent processes these results using `get_company` to fetch the exact matching ratios and submission steps. This MCP Server ensures that the returned JSON matching rules map perfectly to your Python models. You won't face silent errors or hallucinated match limits in your production pipelines.

Monitor campaign performance metrics

The `get_dashboard_stats` tool delivers aggregate match statistics to help your agent analyze fundraising performance. Your agent cross-references these stats with `list_campaigns` to track active corporate initiatives. This lets you run automated audits of your matching gift pipeline. You get reliable, typed data structures for every single campaign metric.

Setup guide

Set up Double the Donation 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": {
        "double-the-donation-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Double the Donation transactions")
print(result.output)

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Common questions about Double the Donation MCP in Pydantic AI

Install the dependency using `pip install "pydantic-ai-slim[mcp]"` and initialize `MCPToolset` with your server's HTTP endpoint. Pass this toolset into the `toolsets` parameter of your `Agent` to instantly expose the MCP Server.

Yes, the agent uses `list_subsidiaries` to trace corporate hierarchies when a donor names a local branch. This ensures the matching rules of the parent company are correctly mapped and validated.

The Pydantic AI validation layer will raise a validation error immediately. This loud failure prevents corrupt matching data from leaking into your database or confusing your donors.

You call `get_donation` to fetch specific transaction records for validation. The agent parses the response into typed Python models, ensuring absolute structural correctness.

All donation records and transaction details passing through the server are processed in an isolated, ephemeral runtime environment. No transaction histories or donor names are ever cached on the Vinkius hosting layer.

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