Teachworks MCP Server for LlamaIndexGive LlamaIndex instant access to 6 tools to Create Student, Get Student, List Families, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Teachworks 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 Teachworks app connector for LlamaIndex is a standout in the Calendar Scheduling category — giving your AI agent 6 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 Teachworks. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Teachworks?"
)
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 Teachworks MCP Server
Connect your Teachworks tutoring management account to any AI agent and simplify how you coordinate your education business, student directory, and lesson scheduling through natural conversation.
LlamaIndex agents combine Teachworks tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Student Management — List all enrolled students, create new student profiles, and retrieve detailed academic metadata.
- Teacher Coordination — Query your directory of tutors and teachers to manage staff assignments and availability.
- Lesson Scheduling — List all scheduled lessons and classes to monitor your academy's teaching calendar.
- Family Oversight — List and manage customer families to maintain organized billing and contact records.
- Profile Insights — Fetch detailed profile information for individual students using their unique IDs.
- Operational Monitoring — Check your education ecosystem status and teacher distributions directly from the agent.
The Teachworks MCP Server exposes 6 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 6 Teachworks tools available for LlamaIndex
When LlamaIndex connects to Teachworks through Vinkius, your AI agent gets direct access to every tool listed below — spanning tutoring-management, lesson-scheduling, student-tracking, 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 a new student
Get student details
List families
List scheduled lessons
List all students in Teachworks
List all teachers (tutors)
Connect Teachworks to LlamaIndex via MCP
Follow these steps to wire Teachworks 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 Teachworks MCP Server
LlamaIndex provides unique advantages when paired with Teachworks through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Teachworks tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Teachworks tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Teachworks, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Teachworks tools were called, what data was returned, and how it influenced the final answer
Teachworks + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Teachworks MCP Server delivers measurable value.
Hybrid search: combine Teachworks real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Teachworks 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 Teachworks for fresh data
Analytical workflows: chain Teachworks queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Teachworks in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Teachworks immediately.
"List all active students in my Teachworks account."
"Show me the teaching schedule for this week."
"Create a new student record for 'Mike Ross' (mike@example.com)."
Troubleshooting Teachworks MCP Server with LlamaIndex
Common issues when connecting Teachworks to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTeachworks + LlamaIndex FAQ
Common questions about integrating Teachworks MCP Server with LlamaIndex.