Embedding Dimension Optimizer Connector for AI agents.
3 live capabilities
Optimize vector embedding dimensions for faster retrieval and lower storage costs
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Why people use Embedding Dimension Optimizer
Stop overpaying for vector storage with Embedding Dimension Optimizer
With this MCP, you stop guessing. You can run mathematical checks to see exactly how much you'll save in bytes and milliseconds by shrinking your vectors. You get the exact same intelligence with a much smaller footprint.
What Vinkius changes
You get a mathematically backed recommendation for your vector embedding configuration.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Scaling a RAG pipeline
An engineer realizes their vector database is getting too expensive as they scale to millions of vectors.
- Real-world use case 02
Meeting strict latency SLAs
A developer building a real-time search capability needs sub-100ms response times.
- Real-world use case 03
Validating new model deployments
Before switching to a smaller, cheaper embedding model, a data scientist uses validate_task_suitability to make sure the new model won't cause the agent to lose context on complex queries.
Complete set · 3capabilities
The complete Embedding Dimension Optimizer capability set.
These are the exact actions your AI can choose when you ask it to work with Embedding Dimension Optimizer.
01—03
3 capabilities in this set.
Part of 3 available through Embedding Dimension Optimizer.
- 01 Capability
Find optimal dimensions
Selects the best dimension from a provided list based on your accuracy and latency needs. It prevents over-provisioning by finding the smallest viable size.
- 02 Capability
Estimate impact of reduction
Calculates the exact storage savings and speed improvements for a specific dimension change. It gives you hard numbers on cost and performance gains.
- 03 Capability
Validate task suitability
Confirms if a model configuration meets the precision requirements for your specific use case. It stops you from using models that are too weak for high-stakes tasks.
Set up in minutes
One URL. Then ask Embedding Dimension Optimizer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Embedding Dimension Optimizer 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_EVWtDbycqbTCXSEmo4nhShFkpo4qThjw3Q7LamEz/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 Dimension Optimizer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Embedding Dimension Optimizer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_EVWtDbycqbTCXSEmo4nhShFkpo4qThjw3Q7LamEz/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 Dimension Optimizer URL.
- Step 03
Save and start
Save the connection and enable Embedding Dimension Optimizer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-dimension-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_EVWtDbycqbTCXSEmo4nhShFkpo4qThjw3Q7LamEz/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 Dimension Optimizer
Open Agent mode in chat and ask: "Using Embedding Dimension Optimizer, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-dimension-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_EVWtDbycqbTCXSEmo4nhShFkpo4qThjw3Q7LamEz/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 Dimension Optimizer
Ask Copilot: "Using Embedding Dimension Optimizer, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-dimension-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_EVWtDbycqbTCXSEmo4nhShFkpo4qThjw3Q7LamEz/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 Dimension Optimizer
Open Cascade and ask: "Using Embedding Dimension Optimizer, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"embedding-dimension-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_EVWtDbycqbTCXSEmo4nhShFkpo4qThjw3Q7LamEz/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 Dimension Optimizer
Ask Cline: "Using Embedding Dimension Optimizer, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add embedding-dimension-optimizer --transport http "https://edge.vinkius.com/vk_preview_EVWtDbycqbTCXSEmo4nhShFkpo4qThjw3Q7LamEz/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 Dimension Optimizer
Ask Claude: "Using Embedding Dimension Optimizer, show me...". 3 tools are ready
Where the request belongs
Work Embedding Dimension Optimizer can move forward.
This is for engineers and data scientists who are managing large-scale vector databases and need to balance cost against retrieval performance.
Machine Learning Engineer
Optimizing embedding models to ensure high retrieval accuracy while keeping inference costs low.
Data Engineer
Managing the storage footprint and query latency of massive vector databases used in RAG pipelines.
AI Architect
Designing scalable retrieval systems that need to maintain semantic integrity at scale.
Build the capability set
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Embedding Similarity Calculator
Calculate mathematical distances and similarity scores between multidimensional numerical vectors.
Vector Similarity Threshold Enforcer
Compute exact vector similarity scores and enforce strict relevance thresholds for RAG pipelines.
Bring your own AI
Change the model, client or framework. Keep Embedding Dimension Optimizer connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
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LibreChat -
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Chorus -
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Vercel AI SDK
Before you connect
Questions about Embedding Dimension Optimizer.
The practical details behind the request, access and result.
How can I use Embedding Dimension Optimizer to lower my cloud costs?
You can use it to calculate exactly how much storage you'll save by reducing your vector dimensions. It helps you find the smallest possible size that still keeps your AI accurate, which directly lowers your database hosting bills.
Will Embedding Dimension Optimizer make my AI agent slower?
Actually, it's designed to make your agent faster. By finding more efficient dimensions, you reduce the computational work required for every search, which speeds up your retrieval latency.
Can I use Embedding Dimension Optimizer to check if my embeddings are accurate enough?
Yes. You can use it to verify if a specific dimension size or model configuration meets the accuracy thresholds required for your specific application, preventing errors before you deploy.
Is Embedding Dimension Optimizer compatible with my existing vector database?
Yes. This MCP works with the mathematical properties of embeddings, so it can be used to plan changes for any vector database, regardless of which provider you use.
How does Embedding Dimension Optimizer help with RAG performance?
It optimizes the retrieval part of your RAG pipeline. By balancing dimension size against accuracy, it ensures your agent retrieves the most relevant context as quickly as possible.
One connection away
Give your agent a direct line to Embedding Dimension Optimizer.
Connect Embedding Dimension Optimizer once. Keep it beside 6,400+ managed Connectors when the next task needs more.
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