How to Use the Classe365 MCP in LlamaIndex

Q: Does the Classe365 MCP server support LlamaIndex filters?

You can use allowedtools filters to restrict what your indexer can access. This keeps your data access tight and predictable.

Index your Classe365 student data into LlamaIndex for grounded, searchable academic insights.

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Connect Classe365 MCP to LlamaIndex

Create your Vinkius account to connect Classe365 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 Classe365 with LlamaIndex

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Index Classe365 records with LlamaIndex

Call `list_students` and pipe the output into your vector store. You gain a searchable knowledge base built on live academic data. Stop relying on stale documents. Your agent now references the actual records stored in your backend.

Grounded responses for LlamaIndex

Use `list_exam_assessments` to feed performance data into your RAG pipeline. Your agents answer questions based on real scores instead of guessing. It turns your academic database into a conversational asset. You query the index and get precise answers about student progress.

Query historical attendance data

Run `list_student_attendance` to ingest history into your retrieval index. You can search across time to find patterns in attendance records. This setup bridges the gap between your dashboard and your AI agent. You get context-aware answers without manual data dumps.

Setup guide

Set up Classe365 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 Classe365 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 Classe365 tools.",
)
response = await agent.run("List recent Classe365 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 Classe365. 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 Classe365 MCP in LlamaIndex

Use the McpToolSpec class to pull the tools into your agent. Once linked, you can query the results and index them for your search operations.

Yes, by including the tools in your agent definition, you allow the model to fetch and search live records during the retrieval phase.

Your student information is scoped to your specific API token. The server architecture ensures that only your authorized agents retrieve these records.

You can use allowed_tools filters to restrict what your indexer can access. This keeps your data access tight and predictable.

We enforce strict token-based authentication. Your student assessment scores and profile data are never stored on our servers, remaining strictly within your infrastructure.

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