How to Use the Cloud Assess MCP in LlamaIndex

Index Cloud Assess training records into LlamaIndex to query student progress and compliance using RAG.

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Connect Cloud Assess MCP to LlamaIndex

Create your Vinkius account to connect Cloud Assess 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 Cloud Assess with LlamaIndex

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Ground RAG pipelines in live Cloud Assess data

By calling `list_student_members` and `get_student_details`, your LlamaIndex agent indexes student profiles directly into vector storage for semantic querying. This ensures your knowledge base stays current. Instead of relying on static PDF exports, your application queries live enrolment statuses. You get answers grounded in current training data using this MCP Server, eliminating hallucinations about who has completed which units.

Query Cloud Assess MCP Server records via RAG

Your agent uses `list_assessment_records` and `list_assessment_forms` to pull specific checklist answers into your index. This lets you search through raw evidence programmatically. You can run natural language queries like finding which students failed a safety checklist. The agent searches the indexed assessment evidence to pinpoint exactly where trainers flagged issues.

Map enrolment progress to training resources

Your LlamaIndex agent uses `list_unit_enrolment_progress` and `list_training_enrolments` to build a real-time status matrix. This aligns student progression with physical campus capacity. It cross-references progress with `list_training_locations` to identify which facilities are bottlenecked. The MCP Server provides the live-updated snapshot of training distribution.

Setup guide

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

Install the LlamaIndex MCP tool wrapper and initialize the MCP client. Convert the client to tools using the tool spec and pass them to your FunctionAgent.

Yes. You can run a script that calls the assessment tools, wraps the JSON in Document objects, and builds a vector index.

The agent uses function calling to select tools like progress checks when to call the MCP Server based on user queries about student status or training milestones.

You can use the allowed tools list in your tool spec to restrict the agent, limiting it to read-only progress tools.

All API calls fetching student details and training progress run through secure, ephemeral V8 isolates. Your sensitive HR and training data is never cached or used for model training.

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