How to Use the Didacte MCP in LlamaIndex

Q: Can Didacte tools be filtered in LlamaIndex?

You can use the allowedtools filter during initialization. This restricts the agent to only use specific tools like searchcoursesbytitle while excluding administrative functions.

Q: Is Didacte integration fast for large datasets?

Performance depends on your indexing strategy. By calling listlmscourses once and caching the results, you keep the agent responsive even with massive course catalogs.

Index your Didacte LMS content into vector stores using LlamaIndex for intelligent, data-backed answers.

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Connect Didacte MCP to LlamaIndex

Create your Vinkius account to connect Didacte 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 Didacte with LlamaIndex

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Build a searchable knowledge base

Convert course data from `list_lms_courses` and `list_course_curriculum` into a searchable vector index. This allows LlamaIndex to retrieve specific lesson information instantly. Your RAG application no longer relies on static files. It queries the live LMS environment to provide accurate, up-to-date answers about your curriculum.

Ground AI responses in actual enrollments

Map user enrollment status by combining `list_course_enrollments` with your existing document indices. The agent finds the intersection between user needs and current course access. This approach eliminates hallucinations. Every answer the agent provides is backed by the current state of your organization's Didacte registry.

Analyze learning profiles semantically

Index the output of `get_user_learning_profile` to make learner history a part of your agent's long-term memory. You can ask questions about trends across your entire user base. LlamaIndex treats these profiles as context, allowing the agent to suggest courses based on previous performance. It creates a personalized experience without manual profile analysis.

Setup guide

Set up Didacte 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 Didacte 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 Didacte tools.",
)
response = await agent.run("List recent Didacte data")

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Common questions about Didacte MCP in LlamaIndex

The server provides raw data which you ingest into your vector store. Once indexed, you can perform semantic searches across all your course and enrollment records.

You can use the allowed_tools filter during initialization. This restricts the agent to only use specific tools like `search_courses_by_title` while excluding administrative functions.

Performance depends on your indexing strategy. By calling `list_lms_courses` once and caching the results, you keep the agent responsive even with massive course catalogs.

Yes. The tools are fully compatible with async patterns, ensuring your LlamaIndex application remains non-blocking while waiting for API responses.

Data is processed locally within your LlamaIndex pipeline. We use scoped access to ensure only the requested `get_user_learning_profile` info enters your vector store, keeping sensitive records off public indexes.

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