How to Use the Bloomerang MCP in Pydantic AI

Bring type-safe Bloomerang data into your Pydantic AI agent.

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Connect Bloomerang MCP to Pydantic AI

Create your Vinkius account to connect Bloomerang 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 Bloomerang with Pydantic AI

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Perplexity

Validate Bloomerang data with Pydantic AI

Every response from `get_constituent` is checked against your Pydantic models at runtime. If the API returns a unexpected field, your agent fails immediately rather than processing bad data. This ensures your CRM logic stays predictable. You define the types, and the MCP Server fills them with live data from your account.

Track campaign success using Pydantic AI

Your agent pulls `list_campaigns` and maps them to your local data structures. It enforces strict type checks on all fundraising metrics, preventing runtime errors during analysis. This creates a robust pipeline for data-driven decisions. You always know exactly what the data looks like before your code acts on it.

Automate constituent workflows with Pydantic AI

Use `list_transactions` to pull gift history into your agent. Pydantic AI verifies the transaction schema, so your agent only works with accurate, well-formed donation records. This approach removes the guesswork from agent-driven CRM updates. You get clean, validated donor information every time you run a query.

Setup guide

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

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

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

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Common questions about Bloomerang MCP in Pydantic AI

It guarantees that the data received from the CRM matches your type definitions. If the response structure changes, your agent catches the mismatch immediately.

You instantiate an MCPToolset with your Vinkius HTTP URL. Then, pass that toolset into the agent constructor to make the CRM functions available for use.

The server operates within a V8 isolate, meaning your constituent records are never exposed to other processes. Access is governed by your unique endpoint token, and the session is entirely ephemeral.

Yes, the setup accepts both Streamable HTTP and SSE transports. You choose the one that fits your deployment requirements, and the connection remains persistent for the agent's lifetime.

It provides JSON-serialized constituent profiles and transaction records. Your agent treats these as standard Python objects once the Pydantic models validate the incoming fields.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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