How do I get a DigitalChalk API Key?

Log in to your DigitalChalk account, navigate to the API section in your settings, and you can generate or retrieve your unique API Key from there.

What is the subdomain?

It is the first part of your DigitalChalk URL. For example, if you access your portal at 'mycompany.digitalchalk.com', your subdomain is 'mycompany'.

Can the agent see exam answers?

The agent can retrieve overall exam results and grades, but specific question-level answers and individual choices are viewed within the DigitalChalk platform.

DigitalChalk MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: DigitalChalk
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="DigitalChalk Specialist",
    goal="Help users interact with DigitalChalk effectively",
    backstory=(
        "You are an expert at leveraging DigitalChalk 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 DigitalChalk "
        "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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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 DigitalChalk MCP Server

Integrate DigitalChalk, the comprehensive learning management system (LMS), directly into your AI workflow. Manage your course offerings, monitor student enrollments and completion statuses, and track exam results using natural language.

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

What you can do

Offering Oversight — List and retrieve detailed settings for all your active course offerings in the catalog.
Learner Intelligence — Access detailed profiles for students and administrators and track their learning history.
Progress Tracking — Monitor individual enrollment progress and identify recently completed courses.
Assessment Monitoring — List exams and quizzes and track results to ensure academic compliance.

The DigitalChalk 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 DigitalChalk to CrewAI via MCP

Follow these steps to integrate the DigitalChalk 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 DigitalChalk

Why Use CrewAI with the DigitalChalk MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with DigitalChalk 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 the 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

DigitalChalk + CrewAI Use Cases

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

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

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

DigitalChalk MCP Tools for CrewAI (10)

These 10 tools become available when you connect DigitalChalk to CrewAI via MCP:

get_lms_account_metadata

Retrieve metadata and settings for your DigitalChalk account

get_offering_details

Get detailed settings and information for a specific course offering

get_user_learning_profile

Get full profile and enrollment history for a specific user

list_assessment_exams

List all exams and quizzes defined in the system

list_course_offerings

List all available course offerings in your DigitalChalk catalog

list_high_performing_learners

Identify enrollments with a grade above a certain percentage (mock logic)

list_lms_users

List all students and administrators registered in your DigitalChalk account

list_recent_course_completions

Identify enrollments that have been recently completed (mock logic)

list_user_enrollments

List all courses a specific user is currently enrolled in

search_users_by_identity

Search for a user by their full name or email address

Example Prompts for DigitalChalk in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with DigitalChalk immediately.

"List all active course offerings."

"Show me the grade for user 'John Doe' in 'Business Ethics'."

"Search for users with email '@example.com'."

Troubleshooting DigitalChalk MCP Server with CrewAI

Common issues when connecting DigitalChalk 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

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

DigitalChalk + CrewAI FAQ

Common questions about integrating DigitalChalk 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.