NETSCOUT adds MCP links to Omnis AI Insights platform suite

NETSCOUT has added Model Context Protocol connectivity to its Omnis AI Insights product, allowing AI assistants and agents to access the company's Smart Data at run time.

The update expands how customers can use network data gathered through NETSCOUT's existing data platform in AI systems, analytics tools, and operational software.

Omnis AI Insights is built around two products: Omnis Sensor and Omnis Streamer. Omnis Sensor captures network information at observation points and extracts application, service, transaction, and behavioural context in real time. Omnis Streamer then collects and prepares that data for downstream use.

With the new MCP server built into Omnis Streamer, prepared data can now be delivered directly to AI assistants and agents on demand. According to NETSCOUT, this gives those systems access to trusted operational evidence rather than raw telemetry alone.

Smart Data is based on the company's Adaptive Service Intelligence technology. NETSCOUT says it performs semantic extraction and context optimisation before data moves into downstream systems, aiming to turn detailed network information into smaller datasets that AI models and other platforms can use more easily.

Data pipeline

The move reflects a wider industry effort to improve the quality of data fed into AI systems as companies try to reduce the cost and uncertainty of working with large volumes of raw operational information. Rather than relying only on broad monitoring signals, vendors are increasingly supplying AI tools with curated data tied to specific operational events.

The product supports both direct access for AI systems through MCP and integration with external platforms including Splunk, ELK Stack, Datadog, ServiceNow, and Dynatrace. Existing customers can add the new functions through Omnis Sensor Adaptors without replacing current NETSCOUT infrastructure.

NETSCOUT has also created playbooks to shape datasets for different industries and operating environments, including healthcare, financial services, and telecommunications service providers.

A central argument behind the launch is that preparing and enriching network evidence before it reaches an AI model can reduce the amount of data that must be processed later. NETSCOUT argues that this can lower token usage and infrastructure demands while giving observability, security, and analytics tools more precise operational inputs.

Operational context

Phil Gray, AVP, Product Management, NETSCOUT, outlined the company's position on the role of trusted data in AI-driven operations.

"Everyone knows there is no value to conclusions that cannot be trusted," said Phil Gray, AVP, Product Management, NETSCOUT. "By adding MCP tools alongside our existing Kafka streaming capabilities, Omnis AI Insights gives IT professionals the flexibility to feed AI-ready Smart Data into analytics and AI platforms at scale and cost effectively, while also making that same context-rich intelligence directly accessible to Models and Agents. This helps organizations power AI with a compact, curated, trusted source of network truth rather than fragmented operational signals that suffer from hallucinations and high token spends."

NETSCOUT also pointed to a deployment example in which standard application monitoring tools showed no application errors and no obvious issue to investigate, even though network conditions were affecting user experience. In that case, Smart Data retained specific network evidence, including minimum window size, total retransmit count, and zero-window event count.

The example illustrates NETSCOUT's effort to position network-derived evidence as a factual reference point for AI systems making operational assessments. For IT teams weighing how far to automate decisions in service assurance, observability, and incident analysis, access to underlying network records may offer a way to test or verify what application-level tools suggest.

The new functions are intended to support use across AI, analytics, observability, service assurance, security, and data lake environments. NETSCOUT also says customers can do this without rebuilding data pipelines or moving to a different platform architecture.

The announcement adds to a growing market focus on linking AI tools with operational technology data through standard interfaces. In that context, MCP support may help customers connect AI assistants and agents more directly to live internal data sources while retaining some control over how information is prepared before it reaches a model.

As NETSCOUT put it, Smart Data preserves "exactly what happened across the network, including the minimum window size, total retransmit count, and zero-window event count, allowing AI to verify facts rather than infer reality."