How to Use the GiveForms MCP in Pydantic AI

Q: How does the agent look up a donor by email?

The agent invokes finddonationsbyemail. The tool returns the donor's record, which is automatically validated against your schema before the model reads it.

Build type-safe donor management pipelines with Pydantic AI and GiveForms.

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Connect GiveForms MCP to Pydantic AI

Create your Vinkius account to connect GiveForms 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 GiveForms with Pydantic AI

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Strict runtime validation for donor data

The `get_donation_details` and `find_donations_by_email` tools enforce strict type safety on every record your agent retrieves. If the API returns unexpected data, the framework fails loudly. You won't have to worry about silent database corruption. This strict validation guarantees that your downstream Python code only receives clean data. It makes writing financial reports incredibly predictable.

Model-agnostic integration profiles

The `get_organization_info` tool lets your agent fetch active campaign metadata and map it directly to Pydantic models. You can change models or providers without rewriting your validation schemas. These tools behave exactly the same way whether you use OpenAI or local models. Filtering records with `list_recent_donations` or `list_campaign_donations` returns structured Python objects. Your agent parses them instantly without custom regex or string splitting.

Verify and search donor records safely with MCP Server

The `verify_api_connection` tool checks your pipeline health before your agent runs any queries. Running this check first avoids unnecessary API calls. It keeps your rate limits clean and your logs readable. Searching for donors via `find_donations_by_name` or `list_all_donations` returns validated records every time. Every field is type-checked right at the boundary.

Setup guide

Set up GiveForms 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": {
        "giveforms-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about GiveForms MCP in Pydantic AI

Use the unified toolset constructor pointing to your Vinkius MCP endpoint. Pass this toolset directly into your Agent's toolsets list during initialization.

The framework raises a validation error immediately. This prevents the agent from processing malformed donation records or making incorrect decisions based on bad data.

Yes, you can. Because the framework is model-agnostic, you can route tools like `list_recent_donations` through Ollama or any other local LLM setup.

The agent invokes `find_donations_by_email`. The tool returns the donor's record, which is automatically validated against your schema before the model reads it.

All communication runs over secure, encrypted channels. Your API tokens and donation details are processed in memory within ephemeral, zero-trust sandboxes and are never persisted.

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