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 LangChain 10 tools — connect in under 2 minutes

Name: DigitalChalk
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect DigitalChalk through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "digitalchalk": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using DigitalChalk, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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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.

LangChain's ecosystem of 500+ components combines seamlessly with DigitalChalk through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

Follow these steps to integrate the DigitalChalk MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from DigitalChalk via MCP

Why Use LangChain with the DigitalChalk MCP Server

LangChain provides unique advantages when paired with DigitalChalk through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine DigitalChalk MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across DigitalChalk queries for multi-turn workflows

DigitalChalk + LangChain Use Cases

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

RAG with live data: combine DigitalChalk tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query DigitalChalk, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain DigitalChalk tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every DigitalChalk tool call, measure latency, and optimize your agent's performance

DigitalChalk MCP Tools for LangChain (10)

These 10 tools become available when you connect DigitalChalk to LangChain 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 LangChain

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

Common issues when connecting DigitalChalk to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

DigitalChalk + LangChain FAQ

Common questions about integrating DigitalChalk MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.