Embedding Similarity Calculator Connector for AI agents.
6 live capabilities
Compare and rank multidimensional vectors for high-accuracy search and clustering.
Waiting for input…
Why people use Embedding Similarity Calculator
Embedding Similarity Calculator for Precise Vector Search
With this Connector, your agent can handle the math directly. You just provide the vectors, and it gives you back exact similarity scores or a sorted list of the best matches. You get consistent results every time you run a query.
What Vinkius changes
That it turns raw numbers into actionable rankings and similarity scores.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Semantic Search
A user asks a question, and the agent uses `rank_vectors_by_metric` to find the most relevant document embeddings in a massive database.
- Real-world use case 02
Recommendation Engine
An app needs to find products similar to one a user just bought using `compute_pairwise_metrics` to generate a personalized feed.
- Real-world use case 03
Data Cleaning
A developer runs `validate_vector_dimensions` on a new dataset to make sure it's ready for the production database without dimension errors.
Complete set · 6capabilities
The complete Embedding Similarity Calculator capability set.
These are the exact actions your AI can choose when you ask it to work with Embedding Similarity Calculator.
01—03
3 capabilities in this set.
Part of 6 available through Embedding Similarity Calculator.
- 01 Capability
Calculate cosine similarity
Get exact similarity scores between two multidimensional vectors using the standard cosine formula.
- 02 Capability
Find nearest neighbor matches
Identify the closest entries in a large dataset based on your chosen distance metric.
- 03 Capability
Check dimension consistency
Verify that all vectors in a collection have the same length before you run batch operations.
04—06
3 capabilities in this set.
Part of 6 available through Embedding Similarity Calculator.
- 04 Capability
Rank items by distance
Sort a list of candidate vectors by proximity to a target vector to find the best matches.
- 05 Capability
Compute Manhattan scores
Calculate Manhattan distance to evaluate similarity in high-dimensional spatial data.
- 06 Capability
Validate batch compatibility
Ensure your entire vector dataset is mathematically compatible for group operations.
Set up in minutes
One URL. Then ask Embedding Similarity Calculator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Embedding Similarity Calculator 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_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/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 Embedding Similarity Calculator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Embedding Similarity Calculator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/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 Embedding Similarity Calculator URL.
- Step 03
Save and start
Save the connection and enable Embedding Similarity Calculator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-similarity-calculator": {
"url": "https://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/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 Embedding Similarity Calculator
Open Agent mode in chat and ask: "Using Embedding Similarity Calculator, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-similarity-calculator": {
"url": "https://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/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 Embedding Similarity Calculator
Ask Copilot: "Using Embedding Similarity Calculator, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-similarity-calculator": {
"url": "https://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/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 Embedding Similarity Calculator
Open Cascade and ask: "Using Embedding Similarity Calculator, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-similarity-calculator": {
"url": "https://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/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 Embedding Similarity Calculator
Ask Cline: "Using Embedding Similarity Calculator, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add embedding-similarity-calculator --transport http "https://edge.vinkius.com/vk_preview_Jw1zV0EdxZijh9ns0Ve4DwNFVTYvTKlOlQyDwkeV/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 Embedding Similarity Calculator
Ask Claude: "Using Embedding Similarity Calculator, show me...". 6 tools are ready
Where the request belongs
Work Embedding Similarity Calculator can move forward.
Data scientists and ML engineers who need to build production-ready search or recommendation systems without manual math overhead.
ML Engineer
Building a custom RAG system and needing to rank search results by vector distance.
Data Scientist
Cleaning up vector datasets to ensure all dimensions match before training.
Backend Developer
Implementing a similarity-based filtering system for a production app.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Vector Similarity Threshold Enforcer
Compute exact vector similarity scores and enforce strict relevance thresholds for RAG pipelines.
String Similarity Batch
High-performance string similarity computations for batch processing of large text arrays using algorithms like Levenshtein and Jaro-Winkler.
Keyword Proximity Checker
Analyze text to measure the word distance between keywords for SEO relevance.
Graph Analysis Toolkit
Deep structural analysis of directed and undirected graphs, providing metrics on connectivity, topology, and node importance.
Distance Metrics Engine
Calculate mathematically perfect Cosine, Euclidean, Manhattan, and Chebyshev distances between high-dimensional vectors local. Essential for embedding comparisons.
Exact Levenshtein Distance Calculator Alternative
Compute precise edit distances and string similarity scores.
Bring your own AI
Change the model, client or framework. Keep Embedding Similarity Calculator connected.
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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 -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Embedding Similarity Calculator.
The practical details behind the request, access and result.
What does the Embedding Similarity Calculator do?
It performs mathematical calculations to determine how similar two vectors are. It's used to find the closest matches in a dataset or to rank items by proximity.
How can I use the Embedding Similarity Calculator for a search engine?
You can use it to rank the results of a vector search. Once your agent finds potential matches, this capability sorts them by the most relevant distance.
Can the Embedding Similarity Calculator handle different types of distances?
Yes, it supports several common metrics including cosine similarity, Euclidean distance, dot product, and Manhattan distance.
How does the Embedding Similarity Calculator help with data cleaning?
It allows you to verify that all vectors in a dataset have the same dimensions. This prevents errors when you're preparing data for a production database.
Why should I use the Embedding Similarity Calculator instead of just asking the model?
Standard models can hallucinate math. This capability provides precise, reproducible results every time, which is essential for production-grade search and clustering.
Does the Embedding Similarity Calculator work with my existing vector data?
Yes, as long as your data is in a numerical vector format, this capability can process it to provide similarity scores and rankings.
What metrics are supported?
The calculator supports Cosine Similarity, Euclidean Distance, Dot Product Similarity, and Manhattan Distance. Capabilities available: your_tool_name.
Do all vectors need to have the same size?
Yes, for any mathematical comparison to be valid, all vectors involved must share the exact same dimensionality.
How does ranking work for distance metrics?
For distance-based metrics like Euclidean and Manhattan, vectors with smaller values are ranked higher. For similarity-based metrics like Cosine and Dot Product, larger values are ranked higher.
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