How to Use the DigitalChalk MCP in LlamaIndex

Index your DigitalChalk API data into LlamaIndex for semantic search and grounded knowledge retrieval.

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Connect DigitalChalk MCP to LlamaIndex

Create your Vinkius account to connect DigitalChalk to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup DigitalChalk with LlamaIndex

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Build a searchable knowledge base of LMS data

Use `list_course_offerings` and `get_offering_details` to ingest your catalog into a vector store. Once indexed, your agent searches across all course settings using plain language. LlamaIndex transforms raw API output into a queryable format. You stop guessing where information lives and start asking the data directly.

Query learner profiles with semantic precision

The MCP Server pulls `get_user_learning_profile` data for your agent to analyze. By indexing these profiles, you ask questions like which users are struggling without writing custom filters. Results are grounded in the actual API response. This reduces hallucinations because the model looks at the indexed facts before answering.

Retrieve assessment data via MCP Server tools

Index your `list_assessment_exams` output to keep your agent updated on current quiz structures. You maintain a live view of your LMS content right inside your application. Your agents bridge the gap between static documents and live API data. It's a unified view for all your training records.

Setup guide

Set up DigitalChalk MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all DigitalChalk MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to DigitalChalk tools.",
)
response = await agent.run("List recent DigitalChalk data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DigitalChalk. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about DigitalChalk MCP in LlamaIndex

You use the MCP tool spec to fetch the data and then push it into your vector index. It turns API responses into searchable nodes for your agent.

Absolutely. You combine your API tools with your existing documents in the index. The agent then retrieves relevant information from both sources simultaneously.

You can trigger tool calls whenever you need fresh data. It is up to you to refresh your index with the latest results from the LMS.

The agent converts your natural language question into tool arguments. It then executes the call and uses the returned data to formulate the final answer.

The index resides in your local environment. Only you control the vector store and the API access, ensuring sensitive learner records stay within your infrastructure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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