Build your CX Observability data into everything you run

Most of us open a chat window before we tap a colleague on the shoulder. The answer comes back fast, shaped the way we asked for it. What makes it useful is what the assistant is connected to. An AI assistant without access to your enterprise system is giving you a well-written guess.

The data no other system holds

Operata's MCP Server builds CX Observability data and insights into the context of your enterprise systems, in any chat. That real time, end-to-end data, connected to the systems you use every day, that’s how you balance the speed of scale with control, governance and analytics.

Cut the hours you spend investigating, and catch the trends affecting CX before customers feel them. Correlate and aggregate the data that enables you to find the inefficiencies costing you money, tune your voice AI cohorts, and see where audio quality stopped a bot performing the way it was designed to.

One connection, one standard

Connect Operata to Claude, ChatGPT, Copilot, Cursor, or anything else that speaks the Model Context Protocol. It's one open standard, so there's no custom integration to build and nothing to maintain when you switch assistants.

The assistant reads the live record every time. No exported reports, no second platform login, no stale extract someone pulled last Tuesday.

What you can do with it

Find the calls that matter. Filter traces by attribute, aggregation, and time range.
See the whole root cause. Pull the full OpenTelemetry trace: every span, log, and Operata insight.
Find the trend and spot patterns. Measure trends and outliers across every service.
Get the metric, the definition and the documented fix, without leaving the conversation.
Query the right tenant. Every call, scoped to the group you pick.

Every tool is built for a class of question, so you get the same answer to the same question twice. Operata’s MCP returns facts and figures from the interactions you need to watch, as well as the context from our knowledgebase. There’s no guessing.

Telemetry tells you what happened. Operata's knowledge and documentation tells you what the measure means, what good looks like, and how to fix it. Your AI assistant reads both, gathering everything you need to point to one clear answer.

Every Operata answer traces to your telemetry and our documentation. What data your other systems contribute, they own.

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Telephony errors and audio quality

CONTEXT

CONNECTORS

Operata MCP

pd-trigger

SKILLS

voice-ai-incident-response

I am seeing telephony errors that are impacting bot confidence and apparently causing audio quality issues. What do I do next?

Used Operata MCP, loaded a skill, ran 3 tool calls

MOS on affected legs — baseline 4.2, severe threshold 3.0

Inbound loss — avg 8.4%, peak 14.2% Outbound loss — avg 6.1%, peak 11.7%

Yes, raise a P1 incident

Used pd-trigger integration

Claude Opus 4.1

Operata MCP

3 TOOL CALLS

REQUEST

Build with your AI, grounded in Operata data

A prompt answers today's question. A schedule answers it before you think to ask.

Below are 20 places teams start. Ask the question, run the investigation, get to the root cause in minutes, then keep going.

Operata’s MCP Server is infrastructure your agents build on, so the prompts you write, the projects you save, the skills you add, and the connectors you join all compound from there.

The questions you ask twice become standing artifacts. Set them up once, then let them run. Here's how you could start building the automations that hand you the information you need, when you need it.

Daily, to the operations lead: yesterday's volume, queue waits, transfer hotspots, and flow node failures, each scored against its 7-day baseline. Closes by naming where to flex staff today.

Weekly, to team leads and CX: the five fixable items this week, ranked by cost or CSAT impact, each with the journeys behind it and a suggested fix.

Monthly, to leadership: the cost of time spent in queue, joined with your own finance figures, and a CSAT root cause review clustering low scores by common drivers.

Event-triggered: a queue drifting off its baseline, a campaign about to launch, a change window closing, a named account with a failure on it. These fire on the signal, not the calendar. Each one appears in the list below.

Two rules make a scheduled artifact land. End it with a recommended action and a named owner. Compare it against the last run, so the reader sees whether last week's flagged item moved.

What happened inside the interaction

Operata knows what happened inside the interaction. The agent's device, the network path, the carrier, the WebRTC session, the voice AI turn. No other system in your stack holds that.

Your CRM knows who the customer is, your ITSM knows what changed last night, your WFM knows who was rostered. Each one answers half a question.

We shipped the MCP Server in beta behind an API key, and customers told us the same thing: the value showed up when Operata sat next to their CRM, their ticketing, and their workforce data in the same conversation.

"Take this customer in our CRM, pull their last 20 Operata journeys, and tell me what their experience has been." Until now, answering that meant a project with a business case attached.

Different ways teams are using the MCP

Root cause

Find the root cause

Today your team proves where a problem lives. You gather the MOS trend, the carrier spans, the device telemetry and the change window, then build the case. During an incident that work takes hours you do not have.

Cross-stack root cause

Voice quality dropped in this region over the last two hours. Was it us, the carrier, an agent-side issue, or a platform event?

JoinsYour ITSM and change tooling, through their own connectors.

ReturnsMOS, jitter, packet loss and error spans from Operata, correlated against open incidents and recent changes, with the evidence attached.

Upstream or downstream

Split yesterday's degraded sessions by where the fault sat: carrier path, CCaaS platform, agent network, or endpoint.

JoinsOperata alone.

ReturnsA breakdown by layer, so the escalation goes to the vendor who owns it.

Repeat incident pattern

We have had three quality incidents in this location this quarter. What do they share?

JoinsYour incident history, through its own connector.

ReturnsThe conditions common to all three, and whether the pattern points to a device class, a site, a carrier route or a time of day.

Voice AI failure attribution

Find the bot conversations that failed to contain this week. Separate design failures from speech recognition failures from audio quality failures.

JoinsOperata alone.

ReturnsFailed containment events sorted by cause, with the audio conditions during each turn.

Silent dead ends

Which IVR or flow nodes are dropping customers without an error?

JoinsOperata alone.

ReturnsFlow nodes ranked by abandonment, with the path each customer took to reach them.

Fleet and change

Fleet health and change control

The same data, used before the incident rather than during it.

Agent fleet health

Which agents show a worsening trend in audio quality, CPU pressure or network stability this month, and does it cluster to a device class, browser version or location?

JoinsYour asset register or MDM, to name the hardware.

ReturnsThe trend and the cluster, from Operata device and environment telemetry.

Change and release validation

We pushed a config change on Tuesday night. Compare quality metrics for the affected queues before and after the window.

JoinsYour change management record, which supplies the window.

ReturnsBefore and after evidence instead of anecdote. Sign the change off, or roll it back with a reason.

Endpoint cohort regression

Did the browser update this month degrade WebRTC performance for any cohort of agents?

JoinsOperata alone.

ReturnsSession quality split by browser and version, before and after the rollout date.

Cost

Cost and investment realization

When the board asks whether the investment is performing, the answer travels up through three teams and arrives as a slide someone built by hand. Each question here runs against your own cost figures.

Cost of abandonment

What did abandoned-in-queue interactions cost us last month, by queue, using our cost per minute?

JoinsYour finance or ERP system supplies the rate.

ReturnsA dollar figure per queue, ranked, with the interaction volume behind it.

Hold time reduction model

Model the saving if we cut average hold time by 20% across the top five queues.

JoinsFinance, for cost per minute.

ReturnsA modeled figure with the assumptions stated, so the challenge lands on the assumption rather than the number.

Self-service failure cost

Which self-service flows fail most often, and what does agent handling of those failures add to cost of service?

JoinsFinance, for handling cost.

ReturnsFailed flows ranked by downstream cost, which is a different order to failed flows ranked by volume.

CCaaS investment realization

Which platform capabilities are underperforming against how we configured them, and what is that costing in handle time?

JoinsOperata alone.

ReturnsThe gap between what the platform is set up to do and what the interaction data shows it doing.

Cost of repeat contacts

Show this week's repeat callers, what they were trying to resolve each time, and whether a technical failure sent them back.

JoinsYour CRM, to identify the customer and the case.

ReturnsRepeat contact volume with a cause attached to each, separating process failure from technical failure.

Risk and assurance

Risk, assurance and vendor accountability

Your vendors measure their own SLAs and report on their own performance. These four give you a version of the truth that is yours.

Independent SLA evidence

Build the quality evidence pack for this vendor's SLA review period.

JoinsContract terms, from wherever you hold them.

ReturnsPerformance against each committed measure, sourced from your data rather than theirs.

Baseline drift alert

Alert me when a queue's wait or transfer rate drifts above its 7-day baseline, and attach the likely cause.

JoinsOperata alone.

ReturnsThe drift, the affected queue, and the technical or operational conditions coinciding with it.

Named account watch

For these named accounts, summarize every interaction since the last report and flag anything that went wrong.

JoinsYour CRM supplies the account list.

ReturnsA per-account summary in plain language, with the failures called out.

Campaign readiness check

Before this campaign launches, model expected queue load against current staffing and flag the risk.

JoinsWorkforce management for the roster, marketing for the volume forecast.

ReturnsThe gap between forecast demand and scheduled capacity, per queue and per hour.

Quality

Quality to outcome

Where the interaction data changes what you do about a score, and who you hold responsible for it.

Quality to CSAT correlation

Take last month's lowest CSAT journeys and show me the voice quality, hold time and transfer history behind each one.

JoinsYour CRM or survey platform supplies the scores.

ReturnsEach low-scoring journey with its technical and operational conditions attached, so every score arrives with a cause next to it.

QA scoring

This call was flagged for robotic audio. What were the network and device conditions during it?

JoinsYour QA or recording platform.

ReturnsJitter, packet loss, CPU pressure and connection events for that exact interaction, enough to let the score stand or void it on evidence.

Coaching the cause, not the symptom

Which agents show consistently poor connection stability over the last 30 days, and does it map to location or working pattern?

JoinsWorkforce management supplies role and schedule.

ReturnsAgent experience telemetry grouped by pattern. The intervention becomes a router replacement or an ISP escalation instead of a coaching note a good agent never earned.

Same platform, same permissions

The MCP Server runs on the same foundation as the Operata platform, so it inherits the security, permissions, and reliability you already trust.

You connect once and OAuth carries your Operata permissions across every query run. A team leader sees their team, a BPO sees only their groups, an IT lead sees the infrastructure they manage. Anyone with multiple views switches between them in a single session.

What a person sees in the Operata platform is exactly what their assistant sees through the MCP Server.