Distance Metrics Engine MCP for AI. Get perfect vector math results on your local machine.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
Distance Metrics Engine calculates mathematically exact distances between high-dimensional vectors locally. Need Cosine, Euclidean, Manhattan, or Chebyshev metrics for embeddings? This MCP handles vector math without needing cloud APIs or risking model hallucinations.
It's essential for any ML workflow where accurate similarity scoring is non-negotiable.
What AI agents can do with Distance Metrics Engine Automation
Distance metrics calculate
Calculates exact distances (Cosine, Euclidean, Manhattan) between high-dimensional vectors offline.
Determines how semantically related two high-dimensional vectors are using the cosine formula.
Provides the straight-line distance between two points in a multi-dimensional space.
Calculates the sum of the absolute differences along each dimension, useful for feature comparison.
Finds the largest difference between corresponding components in two vectors, defining the maximum deviation.
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What AI agents can do with Distance Metrics Engine: 1 Tool Available
Use these tools to calculate exact distances and similarities between high-dimensional vectors offline.
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 Distance Metrics Engine on VinkiusDistance Metrics Calculate
Calculates exact distances (Cosine, Euclidean, Manhattan) between high-dimensional vectors offline.
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 Distance Metrics 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 Distance Metrics Engine, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
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Built on the Model Context Protocol (MCP) for 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 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Calculating Vector Distances Used to Be a Pain, Solved with Vinkius AI Gateway
If you work with embeddings, you know the drill. You get two vectors, and you need to measure their semantic distance. Before having this MCP available, teams often fell back on cloud APIs or relied on the AI client's internal math capabilities. This meant either paying extra for every calculation or accepting a score that was only an approximation.
Now? Your agent can call `distance_metrics_calculate` and get the exact metric instantly. It handles all four core distance types—Cosine, Euclidean, Manhattan, Chebyshev—giving you mathematically guaranteed results without ever sending your data outside of your local environment.
The Distance Metrics Engine delivers perfect vector math.
You don't have to worry about approximation errors anymore. You skip the debugging phase where you spent hours figuring out why a similarity score was slightly off, and you jump straight to acting on accurate metrics.
It’s simple: better data means better decisions. This MCP gives your agent reliable math so your ML models actually work as intended.
What your AI can actually do with this
When you work with machine learning embeddings, the numbers matter. Calculating metrics like Cosine Similarity between two 1024-dimensional vectors isn't something your AI client can just guess at; it needs perfect math. This MCP handles that complex vector algebra locally on your CPU, guaranteeing accuracy and total data privacy. You don't have to worry about cloud latency or the agent approximating results incorrectly.
Instead, you get precise distance scores for every common metric—Cosine, Euclidean, Manhattan, and Chebyshev. These metrics are critical when determining how semantically close two pieces of text or feature vectors actually are. Because it runs locally, your embedding data never leaves your machine. Vinkius brings this functionality into the MCP catalog so your agent can access guaranteed mathematical precision right where you work.
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Here's how it actually works
The bottom line is you get mathematically perfect vector math results without any network calls or risk of hallucination.
You pass your high-dimensional embedding vectors into the MCP.
The system runs the required mathematical calculation (e.g., Cosine or Euclidean) locally on your machine's CPU, bypassing external services.
Your agent receives an exact numerical result for the specified distance or similarity score.
Who is this actually for?
Data Scientists, ML Engineers, and Research Analysts who build applications based on semantic search or clustering. If your job relies on accurate document comparison, you need this.
Uses the MCP to validate model performance by running exact metric calculations against test datasets before deployment.
Integrates vector math directly into agent workflows for real-time semantic similarity checks on private client data.
Compares feature vectors across different datasets to measure divergence or clustering boundaries accurately, without sending raw data to a cloud endpoint.
What Changes When You Connect
Avoid model hallucinations. Since the MCP runs calculations locally, you get exact mathematical answers for metrics like Cosine Similarity—you don't have to trust an approximation.
Maintain data privacy. Your embedding vectors and weights never leave your machine; this is critical when working with sensitive client or proprietary data sets.
Handle high dimensions fast. It processes large, complex embeddings (like 1536-dimensional OpenAI vectors) in milliseconds, keeping your workflow moving.
Use a full metric suite. You access Cosine, Euclidean, Manhattan, and Chebyshev distances all from one place; you don't need multiple connections for different types of math.
Streamline complex analysis. Instead of running separate scripts for each distance type, the MCP lets your agent calculate everything in one go.
See it in action
Clustering Outlier Detection
A data scientist needs to know if a new user's feature vector falls outside an established cluster. Instead of relying on approximate metrics, they use the MCP to calculate the exact Manhattan distance between the user and all known centroids, identifying true outliers with certainty.
Semantic Search Validation
A research analyst has two documents represented by vectors and needs to confirm if they are truly 'highly similar.' They ask their agent to calculate the Cosine similarity using this MCP. The resulting exact score confirms semantic relationship much faster than manual comparison.
Feature Drift Monitoring
An ML engineer is monitoring model drift and needs to compare a current feature vector against a historical baseline vector. They use the Euclidean distance calculation via this MCP, getting an exact deviation score that signals if retraining is required.
Vector Database Comparison
A developer building a similarity search tool requires multiple metric checks (e.g., Cosine and Chebyshev) to validate results before showing them to the user. This MCP executes both tests reliably in one step, providing necessary validation data.
The honest tradeoffs
Relying on LLMs for Math
Prompting an agent: 'What is the Cosine similarity between vector A and B?' The agent approximates a score like 0.9, which might be close but isn't guaranteed accurate.
Tell your agent to use the distance_metrics_calculate tool instead. This forces it to run the calculation locally on your CPU for an exact number.
Using Generic Cloud APIs
Connecting to a general math service that charges per call and might have rate limits, or could fail due to network issues.
Use this MCP. It handles the calculation entirely on your machine, meaning no external calls are needed and you control the entire process.
Writing Complex Python Libraries
Spending hours writing local code just to calculate a single distance metric because the functionality isn't available through an easy tool call.
Call the distance_metrics_calculate tool. It wraps the complex math into one simple, reliable function call.
When It Fits, When It Doesn't
Use this MCP if your core logic hinges on knowing exactly how far apart two data points are in a high-dimensional space (e.g., similarity scoring, clustering). You need precise metrics like Cosine or Euclidean distance scores for validation purposes.
Don't use it if you just need simple counting or string manipulation; those tasks belong to standard text processing tools. Also, don't expect it to calculate graph distances (like shortest path in a network); this MCP is focused purely on mathematical vector geometry. When the requirement is strictly limited to these specific geometric metrics and perfect accuracy is required, this engine is your go-to.
Questions you might have
What is the maximum dimension size for distance_metrics_calculate? +
It supports high-dimensional vectors, including 1536-dimensional embeddings. You just need to make sure your input vectors match that length.
Is calculating Cosine similarity with the Distance Metrics Engine secure? +
Yes, it's highly secure because the entire calculation runs locally on your CPU. Your embedding data never leaves your machine.
Can I use distance_metrics_calculate for anything other than embeddings? +
It calculates standard vector metrics (Cosine, Euclidean, etc.). If your data isn't structured as a high-dimensional vector, this MCP won't help.
Does the Distance Metrics Engine calculate all four major distances? +
Yep. It handles Cosine, Euclidean, Manhattan, and Chebyshev metrics in one single tool call for convenience.
How does `distance_metrics_calculate` ensure my proprietary data stays private? +
It computes everything locally on your machine. All vector math happens directly through your CPU, so your embedding vectors and model weights never leave your network. This keeps sensitive information fully contained.
What are the performance expectations when running `distance_metrics_calculate`? +
Performance is fast because the computation bypasses cloud APIs and network latency. It efficiently handles high-dimensional vectors, delivering precise distance metrics in milliseconds.
How do I integrate `distance_metrics_calculate` into my existing AI client workflow? +
You connect your preferred agent via any MCP-compatible client to Vinkius. Once connected, the tool becomes available for direct invocation by your AI client without needing separate API keys or complex setups.
What happens if `distance_metrics_calculate` receives mismatched vector dimensions? +
The MCP validates input arrays before it runs the math. If the vectors don't match in size, it throws a specific error detailing the mismatch. This prevents bad calculations and helps you debug fast.
Is Cosine distance the same as Cosine similarity? +
No, Cosine Distance equals 1 minus Cosine Similarity. The engine returns both exact values in the JSON response so you always have the complete picture.
Can it compare 1536-dimensional embeddings like OpenAI's? +
Yes! It processes any equal-length array instantly. 1536-dimensional vectors are evaluated in milliseconds local, with exact floating-point precision.
What if the two vectors have different lengths? +
The engine enforces a strict validation constraint and throws a clear error. Both arrays must be mathematically equal in length — there is no silent truncation or padding.
We've already built the connector for Distance Metrics Engine. Just plug in your AI agents and start using Vinkius.
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