What can this MCP calculate?

It calculates P99 latency, error rates, and telemetry storage or processing costs.

How does it detect anomalies?

The MCP identifies specific spans or sampling issues that indicate system instability.

Which AI clients can use this MCP?

You can use it with any MCP-compatible client like Claude, Cursor, Windsurf, or VS Code.

Does it help with observability budgeting?

Yes, you can use the estimation capability to project costs for storing and processing telemetry data.

Is the calculation deterministic?

Yes, the MCP uses a deterministic engine for all performance and cost calculations.

How can I check if my system is meeting its performance SLAs?

You can use the analyze_trace_performance capability. It calculates P99 latency and error rates, and will automatically flag a violation if they exceed your specified thresholds.

How does the capability estimate storage costs?

The estimate_observability_costs capability calculates the total storage cost by multiplying the trace volume per hour by the average span size and the number of retention days.

What defines an anomalous span in this server?

Using detect_tracing_anomalies, a span is flagged as anomalous if its duration is greater than three times the standard deviation of all span durations in the dataset.

An astronaut overseeing a futuristic data hub

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Connect Observability Tracing Calculator to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Get precise performance metrics and cost estimates for your distributed systems.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

Waiting for input…

Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Observability Calculator.

01
Analyze trace performance
This capability evaluates key performance metrics and health indicators from your provided traces.
02
Detect tracing anomalies
Use this to find specific spans or sampling issues that point to system instability.
03
Estimate observability costs
This capability calculates the projected cost to store and process your telemetry data.

Real work

What teams actually run

Budget Planning

Calculate how much telemetry storage will cost based on your current sampling rate and retention policy.

Performance Debugging

Identify if P99 latency is exceeding your service level objectives using trace data.

System Health Audits

Detect anomalies in spans that suggest your distributed system is becoming unstable.

Multi-Agent Monitoring

Analyze how different agents in a distributed system are impacting overall trace performance.

One connector, every AI

Observability Calculator works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put Observability Calculator to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

907ms average. Fast in production.

Observability Calculator is checked daily against the live service.

Daily averagePeak 1128ms

Oct 3Today

Fastest day: 674ms
Slowest day: 1128ms
14-day trend: Improving-31%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Observability Calculator, so you can see the experience inside your AI.

It does not authenticate your account with Observability Calculator. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Observability Calculator Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_1WXAMwTDvWAo9gwPxvsjfD55XYplaDAzSL8dINPh/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — Observability Calculator capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "observability-tracing-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_1WXAMwTDvWAo9gwPxvsjfD55XYplaDAzSL8dINPh/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Observability Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Observability Calculator owners hand off.

This MCP is built for engineers managing complex, distributed, or multi-agent architectures.

01
SREs
Hand off trace data to the agent to find performance outliers and system instabilities.
02
DevOps Engineers
Use the agent to project telemetry costs and plan storage budgets.
03
Backend Developers
Ask the agent to analyze P99 latency and error rates for specific service traces.

FAQ

Questions Observability Calculator owners ask.

01
What can this MCP calculate?
It calculates P99 latency, error rates, and telemetry storage or processing costs.
02
How does it detect anomalies?
The MCP identifies specific spans or sampling issues that indicate system instability.
03
Which AI clients can use this MCP?
You can use it with any MCP-compatible client like Claude, Cursor, Windsurf, or VS Code.
04
Does it help with observability budgeting?
Yes, you can use the estimation capability to project costs for storing and processing telemetry data.
05
Is the calculation deterministic?
Yes, the MCP uses a deterministic engine for all performance and cost calculations.
06
How can I check if my system is meeting its performance SLAs?
You can use the analyze_trace_performance capability. It calculates P99 latency and error rates, and will automatically flag a violation if they exceed your specified thresholds.
07
How does the capability estimate storage costs?
The estimate_observability_costs capability calculates the total storage cost by multiplying the trace volume per hour by the average span size and the number of retention days.
08
What defines an anomalous span in this server?
Using detect_tracing_anomalies, a span is flagged as anomalous if its duration is greater than three times the standard deviation of all span durations in the dataset.

Explore

More in Developer Tools

Connect Observability Calculator to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-03

Analyze trace performance

Detect tracing anomalies

Estimate observability costs

What teams actually run

Budget Planning

Performance Debugging

System Health Audits

Multi-Agent Monitoring

Building your own app? The connector is yours to use.

Observability Calculator Connector

Claude Desktop

SREs

DevOps Engineers

Backend Developers

What can this MCP calculate?

How does it detect anomalies?

Which AI clients can use this MCP?

Does it help with observability budgeting?

Is the calculation deterministic?

How can I check if my system is meeting its performance SLAs?

How does the capability estimate storage costs?

What defines an anomalous span in this server?

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