Make your AI work with AI Content Metrics
Connect your account once and let the AI you already use work with it, without building another integration or switching to a different AI. Track Content Volume and Production Speed for SaaS Platforms
4 live capabilities. One account. Your AI. Real work.
- Step 01
Connect
Link your account through Vinkius.
- Step 02
Authorize
You decide what your AI can access.
- Step 03
Pick your AI
Use it with the AI application you already use.
- Step 04
Get things done
Ask your AI to work with your connected account.
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Works with
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Waiting for input…
Why people use AI Content Metrics
AI Content Metrics for AI Agents: Analyzing Content Volume and Throughput
With this MCP, your agent pulls all those numbers automatically. You ask for the total system volume for a given month, and you get a single, definitive metric. It cuts the reporting time from hours to seconds, giving you instant, reliable data.
What Vinkius changes
The bottom line is, you stop guessing about your content performance and start seeing hard, actionable data.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 7,300+ Connectors
- Real-world use case 01
The content team needs to justify budget increases.
The Content Director asks their agent to run `calculate_monthly_throughput` for the last quarter.
- Real-world use case 02
A specific user is underperforming.
The Product Manager uses `get_user_content_summary` to compare User A's output against the team average.
- Real-world use case 03
The content pipeline feels sluggish.
The Operations Engineer runs `measure_generation_velocity` and gets a low score.
Complete set · 4capabilities
The complete AI Content Metrics capability set.
These are the exact actions your AI can choose when you ask it to work with AI Content Metrics.
01—04
4 capabilities in this set.
Part of 4 available through AI Content Metrics.
- 01 Capability
Get user content summary
Provides a high-level overview of the total content volume generated by a specific user.
- 02 Capability
Measure generation velocity
Analyzes the speed of content production, giving you a score based on the AI's efficiency.
- 03 Capability
Calculate monthly throughput
Determines the average volume of content produced per user across the entire system within a monthly timeframe.
- 04 Capability
Evaluate utilization and quality
Measures the practical value of the AI output by comparing how much content was generated versus how often it was actually used.
Set up in minutes
One URL. Then ask AI Content Metrics to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use AI Content Metrics 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_xKPfoLtRGDYmqwLSTYFRRqIrDbwUz0TtSoRymM48/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 AI Content Metrics, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable AI Content Metrics for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_xKPfoLtRGDYmqwLSTYFRRqIrDbwUz0TtSoRymM48/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 AI Content Metrics URL.
- Step 03
Save and start
Save the connection and enable AI Content Metrics in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-content-generation-metrics": {
"url": "https://edge.vinkius.com/vk_preview_xKPfoLtRGDYmqwLSTYFRRqIrDbwUz0TtSoRymM48/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 AI Content Metrics
Open Agent mode in chat and ask: "Using AI Content Metrics, help me...". 4 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-content-generation-metrics": {
"url": "https://edge.vinkius.com/vk_preview_xKPfoLtRGDYmqwLSTYFRRqIrDbwUz0TtSoRymM48/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 AI Content Metrics
Ask Copilot: "Using AI Content Metrics, help me...". 4 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-content-generation-metrics": {
"url": "https://edge.vinkius.com/vk_preview_xKPfoLtRGDYmqwLSTYFRRqIrDbwUz0TtSoRymM48/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 AI Content Metrics
Open Cascade and ask: "Using AI Content Metrics, help me...". 4 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"ai-content-generation-metrics": {
"url": "https://edge.vinkius.com/vk_preview_xKPfoLtRGDYmqwLSTYFRRqIrDbwUz0TtSoRymM48/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 AI Content Metrics
Ask Cline: "Using AI Content Metrics, help me...". 4 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add ai-content-generation-metrics --transport http "https://edge.vinkius.com/vk_preview_xKPfoLtRGDYmqwLSTYFRRqIrDbwUz0TtSoRymM48/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 AI Content Metrics
Ask Claude: "Using AI Content Metrics, show me...". 4 tools are ready
Where the request belongs
Work AI Content Metrics can move forward.
This MCP is for Content Directors, Product Managers, and Operations Engineers who are drowning in content data. If you spend your mornings pulling reports from five different dashboards just to figure out if your AI content efforts are paying off, this is for you. It turns messy data into clear performance scores.
Content Director
Uses this MCP to track overall content volume and quality, ensuring the AI output aligns with brand guidelines and strategic goals.
Product Manager
Checks the system's monthly throughput to justify scaling AI content efforts or reallocating resources to underperforming content types.
Operations Engineer
Monitors generation velocity to identify bottlenecks in the content pipeline and optimize the AI workflow for maximum speed.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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AI Improvement Velocity Tracker
Quantify the speed and effectiveness of your AI model improvement cycles.
Bring your own AI
Change the model, client or framework. Keep AI Content Metrics connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
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LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about AI Content Metrics.
The practical details behind the request, access and result.
How does the AI Content Metrics MCP help me prove content ROI?
It moves you past simple word counts. By running the utilization and quality evaluation, you get a score that compares what was generated against what was actually used by customers, giving you real proof of value.
Can I use the AI Content Metrics MCP to check team performance?
Yes. You can use the user content summary capability to compare individual team members' output. This helps you identify top performers and pinpoint areas where training or process changes are needed.
What if I need to know the total content volume for a whole month?
You can calculate the monthly throughput for the entire system. This gives you a single, reliable number for your executive reports, showing the total scale of your content efforts.
Is the AI Content Metrics MCP good for tracking content speed?
Absolutely. The generation velocity capability analyzes your content pipeline's speed, giving you a score that tells you if your content is being produced efficiently or if there are bottlenecks slowing you down.
Does the AI Content Metrics MCP only count words?
No. It tracks volume, speed, and quality. It assesses the practical value of the output, making sure you're focusing on content that actually drives user action, not just content that exists.
What metrics can I track?
You can track total content volume, generation velocity, monthly throughput, and utilization rates using capabilities like measure_generation_velocity.
How is generation velocity calculated?
The measure_generation_velocity capability calculates speed by factoring in successful generations and subtracting the impact of failed attempts and heavy manual modifications.
Can I see how much a specific user is producing?
Yes, use the get_user_content_summary capability with a specific userId to see their volume, content types, and success rates.
One connection away
Give your agent a direct line to AI Content Metrics.
Connect AI Content Metrics once. Keep it beside 7,300+ managed Connectors when the next task needs more.
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