DigitalChalk 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 DigitalChalk 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 DigitalChalk. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in DigitalChalk?"
)
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 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.
LlamaIndex agents combine DigitalChalk 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
- 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 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 DigitalChalk to LlamaIndex via MCP
Follow these steps to integrate the DigitalChalk 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 DigitalChalk
Why Use LlamaIndex with the DigitalChalk MCP Server
LlamaIndex provides unique advantages when paired with DigitalChalk through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DigitalChalk tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DigitalChalk tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DigitalChalk, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DigitalChalk tools were called, what data was returned, and how it influenced the final answer
DigitalChalk + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DigitalChalk MCP Server delivers measurable value.
Hybrid search: combine DigitalChalk real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DigitalChalk 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 DigitalChalk for fresh data
Analytical workflows: chain DigitalChalk queries with LlamaIndex's data connectors to build multi-source analytical reports
DigitalChalk MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect DigitalChalk to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting DigitalChalk to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDigitalChalk + LlamaIndex FAQ
Common questions about integrating DigitalChalk 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 DigitalChalk 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.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
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.
Type-safe agent development for Python with first-class MCP support.
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 DigitalChalk to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.