Bring CX Observability to your AI Agents

Visit the Operata Docs for a deeper dive into the Operata MCP Server.

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The Operata MCP Server lets you query and retrieve CXobservability insights directly from AI-powered clients such as Cursor, OpenAI Codex, Claude Code, or your own AI agents.

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The Operata MCP server acts as a single, governed bridge - enforcing Operata's permissions and governance rules. An AI Agent can only access the specific data for which its user is authorized.

The Operata MCP Server acts as a bridge between your CX observability data in Operata and any AI agents that support the Model Context Protocol (MCP). Providing structured access to relevant Operata contexts, features, and tools:

Query metrics, logs, traces, errors, and insights

Access context relevant t to the CX, Contact Center, CCaaS, AI Customer Service and Voice AI domains and CX Observability.

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MCP refers to the Model Context Protocol, an open standard for AI systems to connect with external tools and data.

What it is:

A standardized communication protocol that enables large language models (LLMs) to interact with the outside world. It allows AI agents to access new information and use tools like spreadsheets, databases, or code repositories.

How it works:

It acts like a universal translator, enabling a client (the AI application) to connect to an MCP server that handles connections to various tools and data sources, such as Operata. This standardizes communication, avoiding the need for custom integrations for every tool.

Benefits:

It reduces hallucinations by providing access to real-time, reliable data and increases AI utility by enabling more complex tasks and automation.

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Here are some roles an typical use cases (and prompts) that Operata MCP Server could help with:

Technical Operations / NOC

• “Show me which services had the most packet loss in the last 2 hours.”
• “Find all logs mentioning ‘jitter’ or ‘dropped audio’ in the last hour from agent-gateway.”
• “Which traces in the Sydney region had error spans in the last 12 hours?”
• “Drill into the worst trace from sales queue with errors today.”
• “Find anomalies in disconnects by region in the last 7 days.”

Contact Center Operations

• “Compare abandon rate by queue this week vs last week.”
• “Find traces where customers got stuck in the IVR after the VerifyCustomer step.”
• “Which queues had the highest AHT during business hours yesterday?”
• “Show me logs where the transfer latency exceeded 5 seconds.”
• “Which IVR flows are triggering the most disconnects recently?”

Workforce Management / Forecasting

• “Show me call arrival rate by queue every 15 minutes for the last 7 days.”
• “Which queues had a spike in abandon rate after 6pm?”
• “Compare AHT by queue between two custom date ranges.”
• “Find unusual patterns in call volume by hour over the past week.”
• “Which regions are showing rising handle times during lunch hours?”

CX / Product / IVR Teams

• “Which IVR steps have the highest p95 latency in the last 24 hours?”
• “Find traces where the agent answered after a 60s delay.”
• “Show me a timeline of what happened in call CALL-8839.”
• “Compare IVR completion rate for flow X before and after we launched v12.4.”
• “Find logs that mention ‘lex timeout’ from IVR microservice.”

CXOs / Executives

• “Show me MOS, AHT, and abandon rate by platform for the past month.”
• “What changed in customer experience metrics last week compared to the week before?”
• “Summarise the biggest anomalies in the last 7 days across regions.”
• “Which queues are showing improving MOS scores over the last month?”
• “Highlight regions where abandon rate is consistently high.”

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