How to Use the Donately MCP in Pydantic AI

Q: Can I use Pydantic AI to query Donately subscriptions?

Yes. You can use the listsubscriptions tool to get your recurring donation data. Pydantic AI will validate each subscription object to ensure it conforms to your expected format.

Use Pydantic AI to enforce strict type validation on every Donately API response for reliable fundraising operations.

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Connect Donately MCP to Pydantic AI

Create your Vinkius account to connect Donately 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 Donately with Pydantic AI

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Type-safe Donately tool responses

Every response from `get_donation` or `list_campaigns` is validated against Pydantic models at runtime. If the API returns malformed data, your agent stops immediately, preventing silent logic errors. This approach ensures that your agent code only deals with clean, structured objects. You don't have to worry about missing fields or unexpected data types crashing your agent mid-execution.

Strict donor data validation

When your agent fetches donor records using `get_person`, the Pydantic models ensure the data matches your defined schema. This is critical for maintaining accurate contact lists and donation history. Any discrepancy triggers a validation error. You get immediate feedback, allowing you to fix the underlying issue before the agent proceeds with any further actions.

Unified MCP server connectivity

The MCPToolset integrates the Donately server into your workflow with minimal overhead. You simply pass the toolset to your agent, and it gains access to the full suite of 11 tools. Whether you are running local models or cloud-hosted ones, the validation logic remains constant. It gives you the confidence that your agent is acting on verified data, regardless of the model backing it.

Setup guide

Set up Donately 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": {
        "donately-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Donately MCP in Pydantic AI

Every tool response is parsed into a Pydantic model. If the data from Donately doesn't match your schema, the framework raises a validation error, preventing the agent from proceeding with bad data.

Yes. You can use the `list_subscriptions` tool to get your recurring donation data. Pydantic AI will validate each subscription object to ensure it conforms to your expected format.

Yes. You can initialize the MCPToolset with your Donately server URL and pass it to your Agent. It's the standard way to connect external tools while maintaining strict type safety.

Only for the tools your agent actually uses. You can define models for `get_donation` or `list_people` to ensure the agent handles donor data correctly.

The server processes donor IDs within the secure Vinkius sandbox. Pydantic AI validates that these IDs are passed correctly between your code and the API, ensuring no data corruption occurs.

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