How to Use the Donately MCP in Pydantic AI

Use Pydantic AI with Donately to ensure every donor record is validated at runtime for total accuracy.

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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 tools for Pydantic AI

Every response from `list_all_donations` is validated against your Pydantic models. If the API returns malformed data, your agent stops rather than processing bad records. This is essential for maintaining clean donor databases. You define the expected structure, and the framework enforces it before your agent makes a single decision.

Validated donor profile access

Your agent retrieves `get_donor_profile` data and ensures it matches your schema. It also uses `list_highest_lifetime_donors` to generate accurate lists that you can trust for reporting. Because you are using Pydantic AI, you get instant validation errors if the data shape changes. This prevents silent failures when your donor data grows or shifts.

Precise campaign monitoring

Check your progress with `get_campaign_performance_summary` while keeping your agent's state consistent. You can search for specific donations with `search_donations_by_email` and be certain the output is structured correctly. This approach gives you total control over the data your agent sees. You avoid hallucinations by forcing the agent to operate within the strict boundaries of your validated models.

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-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

You set up an MCPToolset pointing to the Donately server URL. Pydantic AI then wraps the tools, validating every API response against your defined models automatically.

It provides strict data validation. If the Donately API returns data that doesn't fit your models, the agent rejects it, ensuring your fundraising logic remains sound.

Yes, you can catch validation errors within your agent's logic. This allows you to handle API issues gracefully without crashing your entire fundraising workflow.

The MCP Server only acts as a pass-through for donor names and donation history. All processing happens within your local environment, ensuring your sensitive data never touches our persistent storage.

Yes, the tools are designed to return predictable structures. As long as your Pydantic models align with the API output, the integration remains seamless and reliable.

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