Agent Evaluation Metrics Calculator Connector for AI agents.
3 live capabilities
Quantify autonomous agent accuracy and operational costs
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Why people use Agent Evaluation Metrics Calculator
Stop guessing with Agent Evaluation Metrics Calculator performance data
With this MCP, you replace those guesses with hard math. You can instantly see if your agent is actually reliable or just lucky, giving you the data needed to deploy with actual confidence.
What Vinkius changes
You get a mathematical scorecard for your agent's performance.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Validating a prompt update
An engineer changes a system prompt and uses calculate_performance_metrics to ensure the new version hasn't tanked the F1 score.
- Real-world use case 02
Monitoring production costs
An MLOps lead uses calculate_efficiency_and_latency to see if a recent model switch increased token costs per task.
- Real-world use case 03
Testing agent trustworthiness
A developer uses calculate_calibration_error to check if an agent's 90% confidence actually results in 90% accuracy.
Complete set · 3capabilities
The complete Agent Evaluation Metrics Calculator capability set.
These are the exact actions your AI can choose when you ask it to work with Agent Evaluation Metrics Calculator.
01—03
3 capabilities in this set.
Part of 3 available through Agent Evaluation Metrics Calculator.
- 01 Capability
Calculate calibration error
Calculates the Expected Calibration Error to see if agent confidence is trustworthy. This helps you know when to trust an agent's self-assessment.
- 02 Capability
Calculate efficiency and latency
Analyzes how much time and money each agent task consumes. It tracks token usage and speed to keep your operations profitable.
- 03 Capability
Calculate performance metrics
Computes core accuracy scores like precision and recall for task sets. It identifies if your agent is actually hitting its targets.
Set up in minutes
One URL. Then ask Agent Evaluation Metrics Calculator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Agent Evaluation Metrics 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_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Agent Evaluation Metrics Calculator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator URL.
- Step 03
Save and start
Save the connection and enable Agent Evaluation Metrics Calculator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-evaluation-metrics-calculator": {
"url": "https://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator
Open Agent mode in chat and ask: "Using Agent Evaluation Metrics Calculator, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-evaluation-metrics-calculator": {
"url": "https://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator
Ask Copilot: "Using Agent Evaluation Metrics Calculator, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-evaluation-metrics-calculator": {
"url": "https://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator
Open Cascade and ask: "Using Agent Evaluation Metrics Calculator, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-evaluation-metrics-calculator": {
"url": "https://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator
Ask Cline: "Using Agent Evaluation Metrics Calculator, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add agent-evaluation-metrics-calculator --transport http "https://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator
Ask Claude: "Using Agent Evaluation Metrics Calculator, show me...". 3 tools are ready
Where the request belongs
Work Agent Evaluation Metrics Calculator can move forward.
This is for engineers and researchers who need to prove their agents work reliably before deploying them to production.
AI Engineer
Uses these metrics to fine-tune prompts and validate model changes.
MLOps Engineer
Monitors agent reliability and cost efficiency in production environments.
Product Manager
Uses performance data to decide if an agent is ready for a customer rollout.
Build the capability set
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Bring your own AI
Change the model, client or framework. Keep Agent Evaluation Metrics Calculator connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
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Void -
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Qodo -
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Pieces -
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AnythingLLM -
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Cherry Studio -
LibreChat -
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LangChain -
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CrewAI -
Vercel AI SDK
Before you connect
Questions about Agent Evaluation Metrics Calculator.
The practical details behind the request, access and result.
How can I use the Agent Evaluation Metrics Calculator to improve my agent?
You can use it to identify exactly where an agent is failing. By looking at precision and recall, you'll know if your agent is being too aggressive or too cautious, allowing you to tune your prompts more effectively.
Can the Agent Evaluation Metrics Calculator help me save money on LLM usage?
Yes. It provides specific data on token usage and cost efficiency, helping you identify which agent workflows are becoming too expensive to run at scale.
Does the Agent Evaluation Metrics Calculator work with any AI client?
Yes, it is designed to work with any MCP-compatible client like Claude, Cursor, or Windsurf, making it easy to add math-based evaluation to your existing workflow.
How do I know if my agent's confidence is real using Agent Evaluation Metrics Calculator?
The capability calculates the calibration error. If this number is low, it means when your agent says it is 90% sure, it is actually right about 90% of the time.
Can I use Agent Evaluation Metrics Calculator to detect if a model update broke my agent?
Absolutely. You can compare the performance metrics of a new model version against your previous baseline to see if accuracy or speed has regressed.
What metrics can I calculate?
You can calculate accuracy, precision, recall, F1 score, task completion rate, average and p95 latency, token efficiency, cost efficiency, regression detection, and Expected Calibration Error (ECE).
How does the regression detection work?
By using calculate_performance_metrics, you can provide a baseline accuracy. The capability will flag a regression if the current accuracy drops by more than 5% compared to that baseline.
Can I measure how much my agent costs to run?
Yes, the calculate_efficiency_and_latency capability calculates cost efficiency by dividing the number of successful tasks by the total compute units used.
One connection away
Give your agent a direct line to Agent Evaluation Metrics Calculator.
Connect Agent Evaluation Metrics Calculator once. Keep it beside 6,400+ managed Connectors when the next task needs more.
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