DonorsChoose MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add DonorsChoose as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to DonorsChoose. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in DonorsChoose?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
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.
LlamaIndex agents combine DonorsChoose tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the DonorsChoose MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the DonorsChoose MCP Server
LlamaIndex provides unique advantages when paired with DonorsChoose through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DonorsChoose tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DonorsChoose tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DonorsChoose, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DonorsChoose tools were called, what data was returned, and how it influenced the final answer
DonorsChoose + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DonorsChoose MCP Server delivers measurable value.
Hybrid search: combine DonorsChoose real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DonorsChoose to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying DonorsChoose for fresh data
Analytical workflows: chain DonorsChoose queries with LlamaIndex's data connectors to build multi-source analytical reports
DonorsChoose MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect DonorsChoose to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting DonorsChoose to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDonorsChoose + LlamaIndex FAQ
Common questions about integrating DonorsChoose MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect DonorsChoose with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect DonorsChoose to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.