How to Use the Limble CMMS MCP in LlamaIndex

Q: Can I filter Limble CMMS tools in LlamaIndex?

Yes. Use the allowedtools filter to limit which functions your agent can execute. This keeps your indexing focused on only the data you need.

Turn Limble CMMS data into a searchable knowledge base for LlamaIndex. Ground your AI responses in actual maintenance records.

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Connect Limble CMMS MCP to LlamaIndex

Create your Vinkius account to connect Limble CMMS 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 Limble CMMS with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Index Limble CMMS for semantic search

Use `list_labor_entries` to build a searchable index of past technician work. Your agents query this history to find how specific mechanical issues were solved previously. This turns raw logs into actionable intelligence. By indexing these entries, you stop relying on generic training data and start using your facility's unique history.

Deep dive into asset history

Retrieve granular details using `get_asset_details` and store them in your vector database. Your agent answers questions about equipment performance based on the latest API data. When a machine goes down, you have instant access to its full record. No more digging through spreadsheets or disconnected logs.

Map locations and asset parts

Query `list_locations` and `list_asset_part_relations` to understand your facility layout. LlamaIndex maps these relationships so you can ask where parts are stored and what assets they support. This provides a spatial and relational understanding of your maintenance operations. Your agents make smarter decisions when they know exactly where equipment resides.

Setup guide

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

Install the llama-index-tools-mcp package and use the BasicMCPClient. Once you convert the tools to the spec format, they become available for your agents to index.

No, because the tools pull live data directly into your index. Your answers are grounded in the actual API output from your facility's records.

Access is restricted to your specific API key and organization ID. Your facility data is never mixed with other users and stays within your private vector store.

Yes. Use the allowed_tools filter to limit which functions your agent can execute. This keeps your indexing focused on only the data you need.

The server retrieves your logs and your application converts them into embeddings. This ensures your private labor data is searchable but remains within your control.

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