Supercharge your AI with Statistics Engine. Run complex math locally. Get mathematically certain metrics.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Connect to your AI in seconds.
The Statistics Engine is a zero-latency server that runs complex mathematical calculations locally within your environment. It instantly computes key descriptive statistics like mean, median, mode, standard deviation, and percentiles on any dataset.
Since it never sends data over the network, you get absolute privacy and mathematically certain results for rigorous analysis.
What your AI can do
Calculate mean
Finds the mathematical average of all numbers in your dataset.
Calculate median
Determines the middle value when all numbers are sorted, ignoring extreme outliers.
Calculate mode
Identifies the number that appears most often in the dataset.
Determine the average (mean), middle value (median), or most common point (mode) of a dataset.
Quantify how spread out your data is using population standard deviation.
Find specific points in the dataset, such as the 95th percentile (p95), to understand outliers and upper bounds.
Ask an AI about this
Compatible AI Apps
OAuth 2.0 Compatible Gemini Waiting for input…
Statistics Engine MCP Server: 5 Tools for Data Analysis
This server provides five distinct functions to calculate fundamental descriptive statistics like the average, middle value, mode, standard deviation, and specific percentiles on any dataset.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Statistics Engine on VinkiusCalculate Mean
Finds the mathematical average of all numbers in your dataset.
Calculate Median
Determines the middle value when all numbers are sorted, ignoring extreme outliers.
Calculate Mode
Identifies the number that appears most often in the dataset.
Calculate Percentile
Calculates a specific point (k-th percentile) to show where data falls within its...
Calculate Standard Deviation
Measures the amount of variation or dispersion in the dataset from the mean.
Connect to your AI in seconds. Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token from
cloud.vinkius.com. For OAuth-protected servers, expand Advanced
settings to add credentials.
Start a conversation
Open a new chat. The Statistics Engine integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Statistics Engine, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by statistics-engine. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 5 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Calculating averages and standard deviations shouldn't require opening five different tabs.
Before the Statistics Engine, calculating basic descriptive metrics was a manual mess. You’d export data to a spreadsheet, use multiple formulas—one for the average, one for variance, one for percentiles—and then copy-paste those results back into your reporting dashboard. If you messed up one formula, the whole report broke.
With this MCP server, you just pass the raw array of numbers to your agent. The agent handles the math in the background using tools like `calculate_median` and `calculate_standard_deviation`. You get the correct, verifiable number instantly, without ever touching a spreadsheet.
Statistics Engine MCP Server: Get Five Key Metrics with One Call
The manual steps of checking for data completeness, calculating basic averages (mean), then running through specialized checks like `calculate_percentile` are all gone. You simply ask your agent to 'describe the dataset' and it runs five specific tools in sequence.
This means you don't just get a number; you get a complete statistical profile of your data, confirming its shape, center, and spread—all deterministically calculated by the local engine.
What your AI can actually do with this
Listen up. The problem with relying on big language models for math is they're unreliable. When you gotta crunch numbers—like metrics, finances, or sensor data—you can't trust an LLM to handle the statistical heavy lifting. They make little errors when they try to aggregate a dataset. Period. This engine fixes that whole mess.
It gives your agent access to a highly optimized computational core that runs math locally within your own environment. That means you ditch trusting AI models for anything involving arrays or precise numbers and start using deterministic functions instead. Best of all? Your sensitive data never leaves your infrastructure. Zero API calls are necessary because the calculations happen right where they live.
Central Tendency: Finding the Core Number
When you need to know what a dataset is centered around, this engine gives you three ways to look at it. You can use calculate_mean if you want the mathematical average of every number in your set; that's simple enough. But sometimes, one huge outlier throws off the mean, right? For instance, if you measure employee salaries and the CEO makes five times what everyone else does, the mean gets skewed fast.
That’s where calculate_median comes into play. It figures out the middle value when all your numbers are sorted, totally ignoring those extreme outliers that mess up a straight average. If you're just trying to pinpoint the most common data point—the number that shows up the most often—you call calculate_mode. These tools let you determine exactly how centered or varied your data is.
Measuring Data Spread: How Wild Is It?
Knowing the average isn't enough. You gotta know if your numbers are clumped together tight or if they're flying all over the place. That’s where calculate_standard_deviation steps in. It measures the amount of variation, or dispersion, in your dataset compared to the mean. A low standard deviation means your data points are grouped close to the average; a high number tells you that the data is spread out—it's wild.
This gives you actual quantitative proof of how consistent your metrics are.
Pinpointing Distribution: Finding Specific Spots
Sometimes, you don't just want the middle, and sometimes you don't even care about the average at all. You might need to know where the bulk of your data falls, or what that really high end looks like without being dragged down by one weird number. That’s why calculate_percentile is crucial.
It lets you calculate a specific point—like the 95th percentile (p95). This tells you exactly where the top 5% of your data falls, which is essential for understanding upper bounds or identifying how extreme an outlier actually is. You can use this to understand what 'normal' looks like within the full range.
The Bottom Line
This engine means you get mathematically certain results every single time. Because everything runs locally, your data stays private. It gives your agent a reliable way to perform rigorous statistical analysis without sending anything over the network. You stop guessing and start knowing exactly what those numbers mean.
019e38f2-eba4-72e7-b895-2c2b43923dea 
Here's how it actually works
The bottom line is: you give it raw numbers, call a specific function, and get back the mathematically correct result without waiting for external APIs.
Provide the server with a clean array of numbers you want to analyze.
Your agent calls the specific tool (e.g., calculate_median) and passes it the data array.
The local core runs the calculation instantly, returning the precise statistical value.
Who is this actually for?
Data Analysts who spend hours cross-referencing dashboards are the primary users. If your job involves calculating performance metrics or spotting data anomalies, you need this engine. It’s for anyone whose business decisions hinge on accurate numbers—especially when simple averages lie to you.
Uses calculate_median and calculate_standard_deviation to spot if an outlier is skewing average metrics, allowing them to report true performance ranges.
Relys on the local nature of the server to process proprietary financial datasets for standard deviation checks without exposing sensitive numbers externally.
Uses calculate_percentile to set service level agreements (SLAs) by determining the 95th percentile latency, rather than just reporting a simple average.
What Changes When You Connect
Absolute Privacy: Because the computation runs locally, your financial or user telemetry data never leaves your machine. Zero API calls are required for analysis.
Mathematical Certainty: Stop trusting LLMs with stats. Use calculate_mean, calculate_median, and others to get results that are 100% mathematically accurate every time.
Outlier Resistance: When data has extreme outliers, the mean lies. Use calculate_median or calculate_mode instead to find a more honest representation of central tendency.
Understanding Spread: Don't just report averages. Run calculate_standard_deviation to show how much your metrics actually fluctuate over time.
Pinpoint Performance: Forget general averages. Use calculate_percentile to determine the 95th percentile latency, which gives you a true picture of worst-case user experience.
See it in action
Debugging System Latency
The Ops Engineer is looking at server logs and sees average latency is low. But they suspect the occasional huge spikes are messing up the service. They run calculate_percentile (p95) on the raw data array to prove that while the mean looks fine, the true experience for most users is much worse.
Analyzing Customer Ratings
A Product Manager needs to know if a bad review skewed their average rating. They use calculate_median on all rating scores. If the median is high, but the mean is dragged down by one or two low numbers, they have proof that outliers are skewing the data.
Identifying Peak Usage Patterns
A Marketing Analyst needs to know what the single most common interaction was last month. Instead of calculating a mean usage time, they use calculate_mode on event logs to pinpoint which specific action (e.g., 'checkout') happened most frequently.
Assessing Workforce Consistency
HR needs to gauge how consistent employee completion times are across a team. They run calculate_standard_deviation on the dataset of submission timestamps. A low standard deviation means high consistency; a high number signals training is needed.
The honest tradeoffs
Using Mean for Skewed Data
An agent calculates the average income from a small town, but one billionaire's salary gets included. The resulting mean suggests high wealth, which is misleading because it only represents 1% of the population.
Don't trust the mean in this case. Use calculate_median instead. The median will ignore that single outlier and give you a much more accurate picture of what most people actually earn.
Treating Simple Average as Enough
A manager just asks for the average website load time (the mean). This hides the fact that 5% of users wait over three seconds, which is a critical failure point.
Always check calculate_percentile. Running calculate_percentile at p90 or p95 shows you the true performance threshold your most frustrated users are actually experiencing.
Ignoring Data Distribution
You calculate both mean and standard deviation but don't know which one to trust. You might assume normally distributed data when it isn't.
Run all five tools together: calculate_mean, calculate_median, calculate_mode, calculate_standard_deviation, and calculate_percentile. Comparing these outputs tells you instantly how skewed or clustered your numbers are.
When It Fits, When It Doesn't
Use this engine if the accuracy of descriptive statistics is non-negotiable. Specifically, use it when your data set might contain outliers (then lean on calculate_median over calculate_mean). Use it to understand range and worst-case scenarios by running calculate_percentile. Don't just run one tool; compare them. For instance, if the mean is 100 but the median is 75, you know your data is heavily skewed—the average is misleading. If you only need basic counts of the most common value, use calculate_mode. Never rely on a single metric to make a major business call; always compare at least three measures from this server.
Questions you might have
How is calculate_median different from calculate_mean? +
The median finds the middle value; the mean calculates the average. If your data has extreme outliers (very high or very low numbers), the mean gets pulled toward those outliers, making the median a more honest measure of what's typical.
Can I use calculate_percentile to find my 90th percentile latency? +
Yes. Using calculate_percentile with '90' as the parameter will tell you that 90% of your measurements were below that value, providing a much tighter service guarantee than just relying on the mean.
Does calculate_standard_deviation account for different data types? +
No. This engine is designed only for numerical datasets. You must pass arrays of numbers to calculate_standard_deviation or any other statistical tool; it won't process text.
Is the calculation done securely using calculate_mean? +
Yes, absolutely. All calculations run locally within your environment (vurb). This means your data never leaves your local infrastructure and isn't sent to a third-party API for processing.
What format should the data be in for calculate_mode to work? +
The input must be a simple array of numbers. The engine accepts standard JavaScript number arrays, so you just pass it an ordered list like [1, 2, 3, 5]. It handles single-dimensional datasets perfectly.
Does calculate_standard_deviation handle very large data sets? +
Yes. Because the calculation runs locally using a highly optimized JavaScript core, it processes massive arrays of numbers without network lag or memory overflow issues you'd see with cloud APIs.
What happens if I use calculate_mean on an empty dataset? +
If you pass an empty array, the tool returns NaN (Not a Number). This predictable error allows your agent to immediately catch invalid inputs and prompt for correct data.
How is the privacy of my data maintained when using calculate_percentile? +
The calculation never leaves your machine. The entire process runs locally, meaning your sensitive metrics or user telemetry are processed entirely on your local computational core. Zero API calls means zero data leaving your network.
Why use this instead of asking the AI to analyze the dataset directly? +
AIs hallucinate complex data calculations because they generate text, not numbers. This MCP provides the AI with a deterministic tool, forcing it to offload the actual number-crunching to a strict JavaScript engine.
Is my data sent to any external service? +
No. The entire engine runs completely local in your local environment. It is "Privacy First" by design, requiring no external APIs or network access.
How does the percentile calculation work? +
The tool sorts your dataset and uses a robust interpolation method to find the exact boundary value below which a given percentage of observations fall. Perfect for p95 or p99 SLA reporting.
We've already built the connector for Statistics Engine. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 5 tools are live and waiting.
You're up and running in seconds.
Gemini Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.