ml-distance Connector for AI agents.
1 live capability
Get mathematically perfect vector similarity and distance scores for your embeddings.
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Why people use ml-distance
Distance Metrics Engine for Accurate Vector Similarity in ML
This Connector changes the game by giving your agent a dedicated math engine. Instead of the AI trying to think through the dimensions, it calls the local distance_metrics_calculate capability. You get the exact decimal point every time, and your workflow stays accurate.
What Vinkius changes
You get mathematically perfect vector math without the AI making up the numbers.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
RAG Similarity Checks
A developer needs to check if a query is relevant to a specific chunk.
- Real-world use case 02
Clustering Analysis
A data scientist wants to know which cluster a point belongs to.
- Real-world use case 03
Privacy-First Vector Search
A company can't send vectors to a cloud API for security.
Complete set · 1capability
The complete ml-distance capability set.
These are the exact actions your AI can choose when you ask it to work with ml-distance.
01
1 capability in this set.
Part of 1 available through ml-distance.
- 01 Capability
Calculate distance
Calculate exact distances (Cosine, Euclidean, Manhattan) between high-dimensional vectors/embeddings offline
Set up in minutes
One URL. Then ask ml-distance to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use ml-distance from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it ml-distance, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable ml-distance for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the ml-distance URL.
- Step 03
Save and start
Save the connection and enable ml-distance in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"distance-metrics-engine": {
"url": "https://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using ml-distance
Open Agent mode in chat and ask: "Using ml-distance, help me...". 1 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"distance-metrics-engine": {
"url": "https://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using ml-distance
Ask Copilot: "Using ml-distance, help me...". 1 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"distance-metrics-engine": {
"url": "https://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using ml-distance
Open Cascade and ask: "Using ml-distance, help me...". 1 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"distance-metrics-engine": {
"url": "https://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using ml-distance
Ask Cline: "Using ml-distance, help me...". 1 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add distance-metrics-engine --transport http "https://edge.vinkius.com/vk_preview_y2uLWGNkX2DGyvuukg2OciAQezxFmCM92SNAHYKa/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using ml-distance
Ask Claude: "Using ml-distance, show me...". 1 tools are ready
Where the request belongs
Work ml-distance can move forward.
Data scientists and ML engineers who need precise similarity scores for RAG systems or clustering without worrying about math hallucinations or data privacy.
ML Engineer
Builds production RAG systems and needs to verify similarity scores accurately.
Data Scientist
Performs clustering on high-dimensional features and needs various distance metrics.
Backend Developer
Integrates vector search features and wants to handle math locally for privacy.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Compute exact Euclidean distances, 2D areas, and 3D volumes completely local. A zero-latency geometry engine for autonomous AI agents.
Haversine Distance Engine
Calculate precise geographic distances between GPS coordinates instantly. Uses the Haversine formula for exact spherical routing.
Embedding Similarity Calculator
Calculate mathematical distances and similarity scores between multidimensional numerical vectors.
Exact Levenshtein Distance Calculator Alternative
Compute precise edit distances and string similarity scores.
Bring your own AI
Change the model, client or framework. Keep ml-distance connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
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Amazon Q -
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Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about ml-distance.
The practical details behind the request, access and result.
Can the Distance Metrics Engine handle OpenAI embeddings?
Yes, it handles 1536-dimensional vectors perfectly. It's designed to handle the high-dimensional math used by major embedding models.
Does Distance Metrics Engine work offline?
Yes, all calculations happen locally on your machine. You don't need an internet connection to run the math.
What's the difference between Cosine and Euclidean in this Connector?
Cosine measures the angle between vectors, while Euclidean measures the straight-line distance. You can use both to get a better picture of your data.
Is Distance Metrics Engine safe for sensitive data?
Absolutely. Because the math happens locally, your sensitive embedding vectors never leave your machine.
How fast is the Distance Metrics Engine?
It processes high-dimensional vectors in milliseconds, making it fast enough for real-time similarity checks in your apps.
Can I use this for RAG systems?
Yes, it's a great way to calculate similarity between a user's query and the document chunks in your database to ensure better retrieval.
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.
One connection away
Give your agent a direct line to ml-distance.
Connect ml-distance once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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