How to Use the Little Green Light MCP in Pydantic AI

Q: How does this MCP Server protect donor contact info?

All data fetched via getdonordetails travels over encrypted HTTPS connections directly between Vinkius and your application. No donor contact info or transaction records are cached or stored on the Vinkius platform.

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Connect Little Green Light MCP to Pydantic AI

Create your Vinkius account to connect Little Green Light 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 Little Green Light with Pydantic AI

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Claude

Perplexity

Validate donor profiles at runtime

`get_donor_details` fetches raw constituent data and parses it directly into strict Python types. The MCP Server exposes this tool so Pydantic AI can enforce these schemas at runtime, meaning your agent cannot proceed with mismatched fields or missing email addresses. If the CRM returns an unexpected data type, the agent raises a validation error immediately. This prevents your pipeline from processing corrupted records or writing invalid data back to your database during automated runs.

Type-safe donation logging with Pydantic AI

`record_new_donation` requires specific fields like constituent ID and amount. Pydantic AI validates these parameters before the HTTP request ever leaves your server, saving API credits and avoiding failed writes. If an agent attempts to write a negative donation amount or a malformed date, the framework intercepts the call. The agent receives a clear validation error, allowing it to self-correct the payload before retrying.

Audit campaigns with strict schema enforcement

`list_donation_gifts` retrieves financial transactions under strict type constraints. Your agent can safely aggregate donation totals knowing that every amount field conforms to a precise float or decimal model. You can cross-reference these gifts with active initiatives using `list_fundraising_appeals`. Because the framework is model-agnostic, you can run these type-safe audits using any local or commercial model without changing your validation code.

Setup guide

Set up Little Green Light 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": {
        "little-green-light-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Little Green Light transactions")
print(result.output)

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Common questions about Little Green Light MCP in Pydantic AI

Install the slim package with the mcp extra, then instantiate the unified toolset class with your Vinkius HTTP URL. Pass this toolset directly into the toolsets parameter of your Agent.

The framework will raise a validation error at runtime. This guarantees that your agent never operates on corrupted constituent data or proceeds with invalid assumptions about a donor's profile.

Yes. Since the framework is lightweight and supports standard Python environments, you can run type-safe queries like `list_donor_constituents` directly from a terminal script using the streamable HTTP transport.

No. Vinkius hosts the server externally. Your Pydantic AI agent connects to the hosted endpoint over HTTP or SSE, meaning you do not need to manage local node or python processes.

All data fetched via `get_donor_details` travels over encrypted HTTPS connections directly between Vinkius and your application. No donor contact info or transaction records are cached or stored on the Vinkius platform.

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