How to Use the Innform MCP in LlamaIndex

Q: Is Innform integration with LlamaIndex reliable for RAG?

It is. You call tools like listusers to pull data directly into your index. This ensures your AI uses current information rather than stale documentation.

Index your live Innform training data into LlamaIndex for grounded, accurate knowledge retrieval.

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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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Index live Innform training status

Feed the output of `list_completed_assignments` into your LlamaIndex knowledge base. Your agent now answers questions based on real, up-to-the-minute data. This removes the guessing game. Your search results are grounded in facts from your LMS.

Query user details with context

Retrieve specific learner info using `get_user_details` and store it in your vector index. You can ask your agent about specific compliance history and get direct answers. It links disparate records into a single source of truth. You stop hunting through different screens for answers.

Automate group data ingestion

Map your organizational structure by calling `list_groups` regularly. LlamaIndex keeps your context window updated with current department assignments. This makes your RAG application aware of your team's actual structure. It simplifies complex queries about training gaps.

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

It is. You call tools like `list_users` to pull data directly into your index. This ensures your AI uses current information rather than stale documentation.

You use the MCP tool spec to wrap the Innform server. This lets your LlamaIndex agent trigger functions like `list_overdue_assignments` on demand.

Yes. By indexing the results of your tool calls, you create a searchable knowledge base of your entire training history.

It does. You can restrict which tools the agent uses, ensuring it only accesses the data you authorize for your index.

Your data remains ephemeral. The server only fetches learner names and assignment status when you explicitly query them, and the connection uses strict TLS.

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