How do I get a DonorsChoose API Key?

For general public access, you can use the API key 'DONORSCHOOSE'. If you require a unique key for high-volume use, you can apply for one through the DonorsChoose developer site.

Can the agent make donations directly?

This integration currently focuses on searching and auditing project data. Making actual donations should be completed via the project links provided by the agent on the DonorsChoose website.

Which geographical areas are covered?

DonorsChoose covers public and charter school classroom projects across all 50 US states.

DonorsChoose MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: DonorsChoose
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect DonorsChoose through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to DonorsChoose "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in DonorsChoose?"
    )
    print(result.data)

asyncio.run(main())

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About DonorsChoose MCP Server

Integrate DonorsChoose, the leading crowdfunding platform for public school teachers, directly into your AI workflow. Search for classroom projects across the US, filter by state, subject, or ZIP code, monitor urgent funding needs, and retrieve detailed information for educational proposals using natural language.

Pydantic AI validates every DonorsChoose tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Project Discovery — Search for classroom projects using keywords, subjects, or specific geographic locations (states and ZIP codes).
Funding Oversight — Monitor projects that are close to their expiration or have high urgency to identify immediate support needs.
Proposal Intelligence — Retrieve detailed information for specific classroom projects, including school details and itemized resource lists.
Newest Opportunity Tracking — List the most recently posted classroom proposals to identify new funding opportunities across the organization.

The DonorsChoose MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect DonorsChoose to Pydantic AI via MCP

Follow these steps to integrate the DonorsChoose MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from DonorsChoose with type-safe schemas

Why Use Pydantic AI with the DonorsChoose MCP Server

Pydantic AI provides unique advantages when paired with DonorsChoose through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your DonorsChoose integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your DonorsChoose connection logic from agent behavior for testable, maintainable code

DonorsChoose + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the DonorsChoose MCP Server delivers measurable value.

Type-safe data pipelines: query DonorsChoose with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple DonorsChoose tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query DonorsChoose and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock DonorsChoose responses and write comprehensive agent tests

DonorsChoose MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect DonorsChoose to Pydantic AI via MCP:

get_classroom_project_details

Get detailed information for a specific classroom project

get_donorschoose_api_metadata

Retrieve metadata for the current API connection

list_high_poverty_needs

Identify projects from schools in high-poverty areas

list_latest_classroom_proposals

List the most recently posted classroom projects

list_projects_by_state

List classroom projects in a specific US state (e.g., NY, CA)

list_projects_by_subject

List projects filtered by subject area (e.g., Literacy, Math)

list_urgent_funding_needs

Identify projects that are close to their expiration or have high urgency

quick_regional_funding_audit

Retrieve a high-level summary of active projects in a region

search_classroom_projects

Search for DonorsChoose classroom projects using keywords

search_projects_by_zipcode

Search for classroom projects within a specific US ZIP code

Example Prompts for DonorsChoose in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with DonorsChoose immediately.

"Search for classroom projects in New York about 'Literacy'."

"Show me urgent projects near ZIP code '90210'."

"List the newest classroom proposals."

Troubleshooting DonorsChoose MCP Server with Pydantic AI

Common issues when connecting DonorsChoose to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

DonorsChoose + Pydantic AI FAQ

Common questions about integrating DonorsChoose MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your DonorsChoose MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.