How to Use the Every.org Charity MCP in Pydantic AI

Q: How do I add the Every.org Charity MCP Server to Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset with your server URL, then pass that toolset into your Agent's toolsets list.

Bring strict type validation to philanthropic data with Pydantic AI.

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Connect Every.org Charity MCP to Pydantic AI

Create your Vinkius account to connect Every.org Charity 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 Every.org Charity with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-safe non-profit queries

The `search_charities` tool queries the non-profit database and returns structured lists of matching organizations. You pass a cause, and the server responds with the data payload. Pydantic AI takes that payload and immediately validates it against your defined schemas. If the API returns a string where an integer should be, the framework throws a loud validation error. You never end up with silent data corruption in your pipeline.

Model-agnostic profile extraction

The `get_charity_details` tool fetches the exact profile and mission data for a single organization. Your agent requests the specifics, and the server delivers the exact record. This strict data retrieval matters when you care about correctness. Because Pydantic AI is model-agnostic, you can swap between Anthropic or local models, and the tool output remains perfectly typed and validated every single time.

Unified MCP Server toolsets

Adding the Every.org Charity MCP Server to your setup uses the unified toolset approach. You instantiate an `MCPToolset` with your HTTP endpoint and attach it to your agent. Forget about the deprecated server classes. This setup handles the transport layer cleanly. Your agent gets immediate access to the tools, and every response gets forced through Pydantic's rigorous runtime checks before your code ever touches it.

Setup guide

Set up Every.org Charity 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": {
        "everyorg-charity-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Every.org Charity transactions")
print(result.output)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Every.org. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Every.org Charity MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Create an MCPToolset with your server URL, then pass that toolset into your Agent's toolsets list.

Yes. Every single response from the search and details tools gets checked against Pydantic models at runtime. If the structure is wrong, the framework fails immediately.

You can. Pydantic AI is completely model-agnostic. You can route the MCP tools through OpenAI, Gemini, or a local model running on your own hardware.

The old HTTP server classes are deprecated. MCPToolset provides a cleaner, more stable connection for Streamable HTTP transports, ensuring your agent actually gets the data it requests.

The server parses search queries and charity IDs. Because it runs on an ephemeral zero-trust architecture, your exact data requests are wiped from memory the moment the Pydantic validation completes.

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