College Scorecard API MCP Server for Pydantic AI 6 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect College Scorecard API through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 College Scorecard API "
"(6 tools)."
),
)
result = await agent.run(
"What tools are available in College Scorecard API?"
)
print(result.data)
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 College Scorecard API MCP Server
Empower your AI agent to orchestrate your entire higher education research workflow with College Scorecard API, the authoritative source for United States school performance and cost data. By connecting the Department of Education's API to your agent, you transform complex college searches into a natural conversation. Your agent can instantly search for schools, audit enrollment metadata, and retrieve detailed program reports without you ever touching a government portal. Whether you are a student planning your future or a researcher monitoring academic trends, your agent acts as a real-time education consultant, ensuring your data is always grounded in official, government-verified records.
Pydantic AI validates every College Scorecard API tool response against typed schemas, catching data inconsistencies at build time. Connect 6 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
- School Auditing — Search for thousands of US colleges and universities by name and retrieve detailed metadata, including location and website links.
- Enrollment Oversight — Retrieve latest student body size and demographics to maintain a clear view of campus scale.
- Geographic Discovery — List schools by state, city, or near specific ZIP codes to identify regional academic hubs instantly.
- Program Intelligence — Query specific fields of study and program data to understand the academic offerings of different institutions.
- Cost Analysis — Retrieve data on tuition and costs to assist in financial planning for higher education.
The College Scorecard API MCP Server exposes 6 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 College Scorecard API to Pydantic AI via MCP
Follow these steps to integrate the College Scorecard API 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 6 tools from College Scorecard API with type-safe schemas
Why Use Pydantic AI with the College Scorecard API MCP Server
Pydantic AI provides unique advantages when paired with College Scorecard API 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 College Scorecard API integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your College Scorecard API connection logic from agent behavior for testable, maintainable code
College Scorecard API + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the College Scorecard API MCP Server delivers measurable value.
Type-safe data pipelines: query College Scorecard API with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple College Scorecard API tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query College Scorecard API and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock College Scorecard API responses and write comprehensive agent tests
College Scorecard API MCP Tools for Pydantic AI (6)
These 6 tools become available when you connect College Scorecard API to Pydantic AI via MCP:
get_college_details
Get full details for a specific college by ID
get_fields_of_study
Get details for specific programs or fields of study
list_colleges_by_city
List schools in a specific city
list_colleges_by_state
List all schools in a specific state
list_colleges_by_zip
List schools near a specific ZIP code
search_colleges
Search for colleges and universities in the US
Example Prompts for College Scorecard API in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with College Scorecard API immediately.
"Search for schools named 'Harvard' using College Scorecard API."
"List colleges in the state of 'NY'."
"What is the student size for school ID 166027?"
Troubleshooting College Scorecard API MCP Server with Pydantic AI
Common issues when connecting College Scorecard API to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiCollege Scorecard API + Pydantic AI FAQ
Common questions about integrating College Scorecard API MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect College Scorecard API with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
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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 College Scorecard API to Pydantic AI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.