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 CrewAI 10 tools — connect in under 2 minutes

Name: DonorsChoose
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to DonorsChoose through Vinkius, pass the Edge URL in the `mcps` parameter and every DonorsChoose tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="DonorsChoose Specialist",
    goal="Help users interact with DonorsChoose effectively",
    backstory=(
        "You are an expert at leveraging DonorsChoose tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in DonorsChoose "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, DonorsChoose becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call DonorsChoose tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the DonorsChoose MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from DonorsChoose

Why Use CrewAI with the DonorsChoose MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with DonorsChoose through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

DonorsChoose + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries DonorsChoose for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries DonorsChoose, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain DonorsChoose tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries DonorsChoose against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

DonorsChoose MCP Tools for CrewAI (10)

These 10 tools become available when you connect DonorsChoose to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

DonorsChoose + CrewAI FAQ

Common questions about integrating DonorsChoose MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.