How to Use the Innform MCP in LlamaIndex

Q: Can I use Innform with LlamaIndex for automated compliance reporting?

Yes, you can build RAG pipelines that query listresults to generate reports. The agent will fetch the raw assessment data, parse it, and summarize the completion rates.

Q: Can I search for individual learner profiles using LlamaIndex?

Yes, the agent can look up specific profiles using getlearner. It then indexes the returned profile details to answer specific questions about that employee's progress.

Index your Innform LMS data directly into vector stores for instant, grounded compliance Q&A using LlamaIndex.

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Connect Innform MCP to LlamaIndex

Create your Vinkius account to connect Innform 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 Innform with LlamaIndex

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Turn training records into searchable indexes

Stop digging through spreadsheets to find out who is certified. LlamaIndex pulls live data using `list_results` and turns those records into vector embeddings. Your agent can search past training performances as if they were standard text files. This setup grounds your queries in real-time facts. When you ask who finished the safety course, the agent checks the indexed output of `get_learner` instead of guessing.

Search company training maps with LlamaIndex MCP Server

Understanding how your training modules align with different offices is tough. This MCP Server lets your indexer query `list_locations` and `list_departments` to build a semantic map of your company structure. By linking these locations to `list_pathways`, your agent answers complex questions about regional compliance. You can instantly identify which offices are falling behind on mandatory training.

Let your agent search your entire course catalog

Finding the right training module shouldn't require manual searches. Your agent can pull the full list using `list_courses` and `list_modules` to build an easily searchable catalog. Users can ask for courses using natural language. The system matches their intent to the tags retrieved via `list_tags` to recommend the correct pathway.

Setup guide

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

You initialize the basic MCP client with your Vinkius URL and convert the server tools into a tool spec. This lets your agent call endpoints like `list_courses` to fetch live data for indexing.

Yes, you can build RAG pipelines that query `list_results` to generate reports. The agent will fetch the raw assessment data, parse it, and summarize the completion rates.

It depends on your pipeline setup. You should configure local vector storage to cache data from `list_learners` so you don't hit rate limits during frequent queries.

Yes, the agent can look up specific profiles using `get_learner`. It then indexes the returned profile details to answer specific questions about that employee's progress.

The sandbox environment encrypts your API key and handles authentication behind the scenes. Your raw learner profiles and department structures are never exposed to the public internet.

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