Mastra AISDK

Statistics Engine MCP Server

Bring Statistical Analysis
to Mastra AI

Name: Statistics Engine MCP Server
Price: 29 USD
Availability: InStock
Rating: 2.2 (1 reviews)
Author: Vinkius

Learn how to connect Statistics Engine to Mastra AI and start using 5 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Calculate MeanCalculate MedianCalculate ModeCalculate PercentileCalculate Standard Deviation

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the Statistics Engine MCP Server?

Large Language Models often struggle with complex statistical aggregations and dataset analysis, leading to subtle analytical errors. The Statistics Engine MCP Server eliminates this risk by equipping your autonomous agents with a highly optimized, local JavaScript computational core.

The Superpowers

Flawless Data Analysis: Calculate mean, median, mode, standard deviations, and percentiles with 100% mathematical certainty.
Absolute Data Privacy: Your sensitive business metrics, financial datasets, or user telemetry never leave your local infrastructure. Zero API calls.
Zero Latency Engine: Process data arrays instantaneously within the local environment without network overhead.

Stop trusting LLMs to do math on arrays. Equip your agent with a real, deterministic statistical engine.

Built-in capabilities (5)

calculate_mean

Calculates the mathematical mean (average) of a dataset

calculate_median

Calculates the median (middle value) of a dataset

calculate_mode

It returns an array of numbers. Calculates the mode (most frequent value) of a dataset

calculate_percentile

Calculates the k-th percentile of a dataset

calculate_standard_deviation

Calculates the population standard deviation of a dataset

Why Mastra AI?

Mastra's agent abstraction provides a clean separation between LLM logic and Statistics Engine tool infrastructure. Connect 5 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.

—
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add Statistics Engine without touching business code
—
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
—
TypeScript-native: full type inference for every Statistics Engine tool response with IDE autocomplete and compile-time checks
—
One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure

See it in action

Statistics Engine in Mastra AI

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Statistics Engine and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Statistics Engine to Mastra AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	Raw MCP	Vinkius
Server catalog	Find and host yourself	4,000+ managed
Infrastructure	Self-hosted	Sandboxed V8 isolates
Credential handling	Plaintext in config	Vault + runtime injection
Data loss prevention	None	Configurable DLP policies
Kill switch	None	Global instant shutdown
Financial circuit breakers	None	Per-server limits + alerts
Audit trail	None	Ed25519 signed logs
SIEM log streaming	None	Splunk, Datadog, Webhook
Honeytokens	None	Canary alerts on leak
Custom domains	Not applicable	DNS challenge verified
GDPR compliance	Manual effort	Automated purge + export

Unlock for AI Agents View step-by-step setup guide for Mastra AI →

Enterprise Security

Why teams choose Vinkius for Statistics Engine in Mastra AI

The Statistics Engine MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 5 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in Mastra AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Unlock for AI Agents View full Statistics Engine details →

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

The Vinkius Advantage

How Vinkius secures
Statistics Engine for Mastra AI

Every tool call from Mastra AI to the Statistics Engine MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

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.