EdApp MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create New Learner, Get Account Info, Get Catalog Statistics, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add EdApp 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 App Connector for LlamaIndex
The EdApp app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 EdApp. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in EdApp?"
)
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 EdApp MCP Server
Connect your EdApp (now SC Training) account to any AI agent and take full control of your corporate training and mobile learning workflows through natural conversation.
LlamaIndex agents combine EdApp tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Learner Orchestration — List and manage your learning community programmatically, including registering new users and retrieving detailed learner profiles
- Catalog Intelligence — Access your complete course catalog and retrieve metadata for lessons and course collections to coordinate training content
- Success Monitoring — Programmatically track course progress and retrieve high-fidelity analytics on completion rates and student engagement
- Interaction Insight — Access detailed logs of lesson attempts and user activity to identify knowledge gaps and coordinate coaching
- Operational Visibility — Check active webhooks and retrieve account metadata directly through your agent for instant L&D reporting
The EdApp MCP Server exposes 12 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.
All 12 EdApp tools available for LlamaIndex
When LlamaIndex connects to EdApp through Vinkius, your AI agent gets direct access to every tool listed below — spanning edapp, microlearning, lms-api, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Add learner to EdApp
Get admin info
Check training stats
Check learner progress
Get user profile
Check lesson interactions
Get event notifications
List grouped content
List course content
List EdApp users
List all courses
Delete learner
Connect EdApp to LlamaIndex via MCP
Follow these steps to wire EdApp into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the EdApp MCP Server
LlamaIndex provides unique advantages when paired with EdApp through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine EdApp tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain EdApp tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query EdApp, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what EdApp tools were called, what data was returned, and how it influenced the final answer
EdApp + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the EdApp MCP Server delivers measurable value.
Hybrid search: combine EdApp real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query EdApp 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 EdApp for fresh data
Analytical workflows: chain EdApp queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for EdApp in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with EdApp immediately.
"List all active training courses in my EdApp account."
"Show me the progress report for course ID 'crs_1'."
"Register 'jane.doe@example.com' as a new learner."
Troubleshooting EdApp MCP Server with LlamaIndex
Common issues when connecting EdApp to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpEdApp + LlamaIndex FAQ
Common questions about integrating EdApp MCP Server with LlamaIndex.