How do I find my College Scorecard API Key?

Register for a free API Key at [**Data.gov**](https://api.data.gov/signup/). Your key will be displayed instantly and sent via email. Copy and paste it below.

Can the agent filter by school state?

Yes. Use the `list_colleges_by_state` tool providing the 2-letter ISO state code (e.g., 'CA' for California). Your agent will return all matching schools in that state instantly.

Is student body size data provided?

Yes. The school records retrieved by your agent include the latest official student size (enrollment) for undergraduate programs where available.

College Scorecard API MCP Server for CrewAI 6 tools — connect in under 2 minutes

Name: College Scorecard API
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 6 Tools Framework

Connect your CrewAI agents to College Scorecard API through Vinkius, pass the Edge URL in the `mcps` parameter and every College Scorecard API 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="College Scorecard API Specialist",
    goal="Help users interact with College Scorecard API effectively",
    backstory=(
        "You are an expert at leveraging College Scorecard API 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 College Scorecard API "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 6 available tools "
        "and what they can do."
    ),
)

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

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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.

When paired with CrewAI, College Scorecard API becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call College Scorecard API 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

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 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 College Scorecard API to CrewAI via MCP

Follow these steps to integrate the College Scorecard API 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 6 tools from College Scorecard API

Why Use CrewAI with the College Scorecard API MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with College Scorecard API 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

College Scorecard API + CrewAI Use Cases

Practical scenarios where CrewAI combined with the College Scorecard API MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries College Scorecard API 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 College Scorecard API, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain College Scorecard API 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 College Scorecard API against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

College Scorecard API MCP Tools for CrewAI (6)

These 6 tools become available when you connect College Scorecard API to CrewAI 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 CrewAI

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

Common issues when connecting College Scorecard API 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.

College Scorecard API + CrewAI FAQ

Common questions about integrating College Scorecard API 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.