Zero-Shot vs Few-Shot Ratio Calculator Connector for AI agents.
3 live capabilities
Measure prompt instruction density and example ratios.
Waiting for input…
Why people use Zero-Shot vs Few-Shot Ratio Calculator
Fixing prompt instability with Zero-Shot vs Few-Shot Ratio Calculator
With this MCP, that manual checking disappears. You get immediate, mathematical proof of how your prompt is structured. You stop guessing and start engineering with actual data on your instruction density.
What Vinkius changes
You get precise structural metrics for every prompt you test.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
The prompt is getting too long
A developer notices high latency and uses calculate_composition_metrics to find that 90% of the prompt is just redundant examples.
- Real-world use case 02
Unpredictable model behavior
An engineer uses classify_prompt_type to realize their 'few-shot' prompt is actually being read as zero-shot due to missing delimiters.
- Real-world use case 03
Inconsistent formatting
A researcher uses audit_demarcation_usage to ensure all prompts in a large batch use the same 'Input:' and 'Output:' anchors.
Complete set · 3capabilities
The complete Zero-Shot vs Few-Shot Ratio Calculator capability set.
These are the exact actions your AI can choose when you ask it to work with Zero-Shot vs Few-Shot Ratio Calculator.
01—03
3 capabilities in this set.
Part of 3 available through Zero-Shot vs Few-Shot Ratio Calculator.
- 01 Capability
Audit demarcation usage
Counts how often specific anchor tokens like 'Input:' or 'Example:' appear. It provides a census of your structural delimiters.
- 02 Capability
Calculate composition metrics
Measures the character weight and ratio between instruction text and example text. Use this to see if your examples are crowding out your instructions.
- 03 Capability
Classify prompt type
Tells you if your prompt is zero-shot, few-shot, or multi-shot. It checks for the presence of demonstration tokens to categorize the approach.
Set up in minutes
One URL. Then ask Zero-Shot vs Few-Shot Ratio Calculator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Zero-Shot vs Few-Shot Ratio 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_FdhYwHjueztYLtmUZVNX6QCKKzSuGeuiMo375QMs/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 Zero-Shot vs Few-Shot Ratio Calculator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Zero-Shot vs Few-Shot Ratio Calculator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_FdhYwHjueztYLtmUZVNX6QCKKzSuGeuiMo375QMs/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 Zero-Shot vs Few-Shot Ratio Calculator URL.
- Step 03
Save and start
Save the connection and enable Zero-Shot vs Few-Shot Ratio Calculator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"zero-shot-vs-few-shot-ratio-calculator": {
"url": "https://edge.vinkius.com/vk_preview_FdhYwHjueztYLtmUZVNX6QCKKzSuGeuiMo375QMs/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 Zero-Shot vs Few-Shot Ratio Calculator
Open Agent mode in chat and ask: "Using Zero-Shot vs Few-Shot Ratio Calculator, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"zero-shot-vs-few-shot-ratio-calculator": {
"url": "https://edge.vinkius.com/vk_preview_FdhYwHjueztYLtmUZVNX6QCKKzSuGeuiMo375QMs/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 Zero-Shot vs Few-Shot Ratio Calculator
Ask Copilot: "Using Zero-Shot vs Few-Shot Ratio Calculator, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"zero-shot-vs-few-shot-ratio-calculator": {
"url": "https://edge.vinkius.com/vk_preview_FdhYwHjueztYLtmUZVNX6QCKKzSuGeuiMo375QMs/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 Zero-Shot vs Few-Shot Ratio Calculator
Open Cascade and ask: "Using Zero-Shot vs Few-Shot Ratio Calculator, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"zero-shot-vs-few-shot-ratio-calculator": {
"url": "https://edge.vinkius.com/vk_preview_FdhYwHjueztYLtmUZVNX6QCKKzSuGeuiMo375QMs/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 Zero-Shot vs Few-Shot Ratio Calculator
Ask Cline: "Using Zero-Shot vs Few-Shot Ratio Calculator, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add zero-shot-vs-few-shot-ratio-calculator --transport http "https://edge.vinkius.com/vk_preview_FdhYwHjueztYLtmUZVNX6QCKKzSuGeuiMo375QMs/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 Zero-Shot vs Few-Shot Ratio Calculator
Ask Claude: "Using Zero-Shot vs Few-Shot Ratio Calculator, show me...". 3 tools are ready
Build the capability set
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Bring your own AI
Change the model, client or framework. Keep Zero-Shot vs Few-Shot Ratio Calculator connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
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LibreChat -
TypingMind -
Chorus -
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n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Zero-Shot vs Few-Shot Ratio Calculator.
The practical details behind the request, access and result.
How can I use Zero-Shot vs Few-Shot Ratio Calculator to save money?
By using the composition metrics, you can identify and remove bloated examples that aren't adding value, directly reducing your token consumption.
Can Zero-Shot vs Few-Shot Ratio Calculator detect broken prompts?
Yes. It can reveal if your prompt is failing to be recognized as few-shot because you missed a critical delimiter like 'Input:'.
Does Zero-Shot vs Few-Shot Ratio Calculator work with Claude or Cursor?
Yes, it works with any MCP-compatible client, including Claude, Cursor, and Windsurf, as long as you connect it via Vinkius.
Will Zero-Shot vs Few-Shot Ratio Calculator help with prompt stability?
Absolutely. By auditing your delimiters and instruction weight, you ensure the model sees a consistent structure every time.
How do I know if my prompt is multi-shot?
You can use the classification capability to instantly verify if the number of demonstrations in your text qualifies as multi-shot.
What is the difference between Zero-Shot and Few-Shot prompting?
Zero-Shot prompting provides only instructions without any examples, while Few-Shot prompting includes demonstrations (input-output pairs) to guide the model's behavior.
How does the capability identify examples in a prompt?
The capability uses exact pattern matching for known delimiters such as 'Example:', 'Input:', 'Output:', 'User:', and 'Assistant:'.
Can I use this to optimize my context window usage?
Yes, by calculating the ratio of example characters to instruction characters, you can identify if your prompts are becoming too dense and consuming unnecessary tokens.
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