Manage RHEL inventory using natural language

Red Hat Lightspeed is transforming how IT professionals interact with complex operational data. By integrating with the Red Hat Lightspeed (formerly known as Red Hat Insights) and Model Context Protocol (MCP), you can simplify inventory management using simple, human-readable language.

This article shares practical examples of how you can perform inventory-related operations using Red Hat Lightspeed and MCP. The core benefit is its ability to turn natural-language prompts into structured inventory API requests. Instead of crafting filters or writing custom scripts, you can simply ask questions and let the AI build and execute the query for you.

Using natural language for inventory querying

Before you begin, ensure your Red Hat Lightspeed and MCP service is correctly set up and running. Refer to the Red Hat documentation for the necessary prerequisites and installation instructions.

The most immediate benefit of Red Hat Lightspeed and MCP is its power to transform complex API queries and data filtering into simple, natural language prompts. The agent utilizes the underlying inventory API tools to fulfill your request.

Behind the scenes, the agent converts your query into a structured API request, retrieves the JSON data from Red Hat Lightspeed inventory, and then uses a large language model (LLM) to summarize and format the result into a readable, conversational response. The allows you to explore your fleet without needing to switch contexts, click through dashboards, or write API queries.

Functionality and corresponding sample natural language prompts:

• Simple filtering: "How many RHEL 9 systems do I have tagged with 'finance'?"
• Complex filtering: "Show me all hosts in the 'Dev' environment running kernel version 4.18.0 that are not currently checked into Satellite."
• Data retrieval: "List the FQDN and last seen date for the 10 oldest RHEL 8 hosts."
• Summary and aggregation: "What is the distribution of my hosts by operating system version?"

Streamlining inventory housekeeping and auditing

Maintaining an accurate, up-to-date inventory is essential for effective IT operations, compliance, and lifecycle management. Traditional data-quality checks—finding stale systems, missing metadata, or duplicate entries—are often time-consuming when done manually.

Red Hat Lightspeed and MCP drastically improves this process by leveraging the LLM's power to proactively audit your inventory data. It quickly surfaces inconsistencies, missing tags, or stale system records, ensuring better configuration management database (CMDB) hygiene.

Housekeeping task and sample prompt to the MCP:

• Find missing data: "Find all inventory hosts missing the 'Cost Center' or 'Owner' tags."
• Identify stale systems: "List all systems that haven't reported data to Red Hat Lightspeed in the last 60 days, excluding those tagged as 'decommissioned'."
• Audit compliance: "Identify all production systems that are not currently running the latest minor RHEL version for their major release."
• Detect potential duplicates: "Show me all hosts that share the same IP address but have different FQDNs, to check for potential duplicates."

By automating these data quality checks, Red Hat Lightspeed and MCP ensures your inventory remains accurate and reliable, a critical requirement for security and compliance reporting. Rather than manually running reports or exporting data, the MCP queries the inventory, flags issues, and summarizes next steps, making CMDB accuracy easier than ever.

Innovative multi-agent orchestration

The true power of Red Hat Lightspeed and MCP lies in its ability to chain together information from different Red Hat Lightspeed services (i.e., inventory, advisor, and vulnerability) to perform multi-step analysis—a process called multi-agent orchestration. This can be accomplished by either enhancing your own code base via using Llama stack or by the use of an AI tool like Langflow.

These innovative prompts require the MCP to use its inventory tool first, and then potentially call a second tool (e.g., Advisor or Vulnerability API) to analyze the returned host list. Few examples of innovative prompts could be:

Multi-agent tasks and corresponding sample prompt to the MCP:

• Proactive remediation planning: "I need to plan my next patch window. Show me all RHEL 8 servers in the 'Staging' environment with at least one high-severity vulnerability, and then summarize the suggested remediation playbook for those systems." (MCP identifies hosts via Inventory, then uses the vulnerability tool to find and summarize the required playbook.)
• Migration readiness analysis: "Analyze the inventory and identify all systems running an OS version that will reach end-of-life (EOL) within the next nine months that are not yet tagged with a 'Migration_Target' label. Group them by their current Business Unit tag." (MCP queries inventory, compares versions/dates against internal knowledge, and generates a structured report.)
• Cross-platform health check: "What is the current system health status (from advisor recommendations) for the top five largest hosts in the 'Critical' group, ranked by their memory size?" (MCP sorts hosts by a resource metric using the inventory tool, retrieves the top five, then queries the advisor tool for their specific health status.)

Join us and share your feedback

Now is a great time to test, experiment, and provide feedback when you connect existing Red Hat Lightspeed and MCP with your LLMs. Whether you're exploring automation, enhancing incident processes, or building intelligent dashboards, this preview places powerful Red Hat Lightspeed capabilities at your LLM-driven fingertips.

This release offers early access to powerful MCP-driven workflows with Red Hat Lightspeed. We strongly encourage your feedback—including bug reports, requests for additional toolsets, and enhancement ideas—through the Red Hat Issue Router (select MCP) and by contributing to our GitHub repository. Your input will directly refine and shape the future of Red Hat Lightspeed and MCP.