Agent Self-Reflection Sentiment Scanner Connector for AI agents.
3 live capabilities
Measure agentic loop stability and self-correction rates
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Why people use Agent Self-Reflection Sentiment Scanner
Stop manual log debugging with Agent Self-Reflection Sentiment Scanner
With this MCP, that manual hunt ends. You point the scanner at your logs, and it instantly pulls out every instance of an agent catching its own mistake. You stop looking at lines of text and start looking at stability scores.
What Vinkius changes
You get a mathematical way to measure how well your agents fix their own mistakes.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Debugging runaway agent loops
An engineer notices an agent is costing too much.
- Real-world use case 02
Validating new agent architectures
A researcher compares two different prompting styles by using calculate_rate to see which one results in more successful self-corrections.
- Real-world use case 03
Production monitoring for autonomous workflows
An MLOps engineer uses get_summary to check the daily health of agent deployments, looking for sudden drops in stability.
Complete set · 3capabilities
The complete Agent Self-Reflection Sentiment Scanner capability set.
These are the exact actions your AI can choose when you ask it to work with Agent Self-Reflection Sentiment Scanner.
01—03
3 capabilities in this set.
Part of 3 available through Agent Self-Reflection Sentiment Scanner.
- 01 Capability
Calculate rate
Computes the statistical frequency of self-corrections relative to the number of loops performed. This gives you a hard number for agent stability.
- 02 Capability
Get summary
Aggregates all scanning and calculation data into a single high-level report. It provides a quick snapshot of an entire execution run.
- 03 Capability
Scan logs
Analyzes raw log files to identify and count specific markers. It finds where agents realize they've failed or succeeded.
Set up in minutes
One URL. Then ask Agent Self-Reflection Sentiment Scanner to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Agent Self-Reflection Sentiment Scanner 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_zVnWh5laIaPjvHbRTMRdjkz3bR3SOhCs4DZa8Sib/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 Self-Reflection Sentiment Scanner, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Agent Self-Reflection Sentiment Scanner for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_zVnWh5laIaPjvHbRTMRdjkz3bR3SOhCs4DZa8Sib/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 Self-Reflection Sentiment Scanner URL.
- Step 03
Save and start
Save the connection and enable Agent Self-Reflection Sentiment Scanner in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-self-reflection-sentiment-scanner-1": {
"url": "https://edge.vinkius.com/vk_preview_zVnWh5laIaPjvHbRTMRdjkz3bR3SOhCs4DZa8Sib/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 Self-Reflection Sentiment Scanner
Open Agent mode in chat and ask: "Using Agent Self-Reflection Sentiment Scanner, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-self-reflection-sentiment-scanner-1": {
"url": "https://edge.vinkius.com/vk_preview_zVnWh5laIaPjvHbRTMRdjkz3bR3SOhCs4DZa8Sib/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 Self-Reflection Sentiment Scanner
Ask Copilot: "Using Agent Self-Reflection Sentiment Scanner, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-self-reflection-sentiment-scanner-1": {
"url": "https://edge.vinkius.com/vk_preview_zVnWh5laIaPjvHbRTMRdjkz3bR3SOhCs4DZa8Sib/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 Self-Reflection Sentiment Scanner
Open Cascade and ask: "Using Agent Self-Reflection Sentiment Scanner, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-self-reflection-sentiment-scanner-1": {
"url": "https://edge.vinkius.com/vk_preview_zVnWh5laIaPjvHbRTMRdjkz3bR3SOhCs4DZa8Sib/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 Self-Reflection Sentiment Scanner
Ask Cline: "Using Agent Self-Reflection Sentiment Scanner, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add agent-self-reflection-sentiment-scanner-1 --transport http "https://edge.vinkius.com/vk_preview_zVnWh5laIaPjvHbRTMRdjkz3bR3SOhCs4DZa8Sib/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 Self-Reflection Sentiment Scanner
Ask Claude: "Using Agent Self-Reflection Sentiment Scanner, show me...". 3 tools are ready
Where the request belongs
Work Agent Self-Reflection Sentiment Scanner can move forward.
This is for the engineers and researchers building autonomous workflows who are tired of manual log debugging and need to prove their agents actually work.
AI Engineer
Uses these metrics to tune agent prompts and logic based on real self-correction data.
MLOps Engineer
Monitors the stability of production agent loops to prevent runaway execution costs.
Agent Researcher
Analyzes how different reasoning architectures impact the frequency of error recognition.
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Bring your own AI
Change the model, client or framework. Keep Agent Self-Reflection Sentiment Scanner connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
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Before you connect
Questions about Agent Self-Reflection Sentiment Scanner.
The practical details behind the request, access and result.
How can the Agent Self-Reflection Sentiment Scanner help me debug agents?
It automates the process of finding where an agent realizes it has made an error. Instead of reading every line, you get a count of how many times the agent self-corrected.
Can I use the Agent Self-Reflection Sentiment Scanner with any agentic workflow?
Yes, as long as your agent produces logs that contain markers for errors and successes, this MCP can parse them to give you stability metrics.
What is a 'self-correction frequency rate' in the Agent Self-Reflection Sentiment Scanner?
It is a mathematical ratio that tells you how often your agent fixes its own mistakes compared to the total number of loops it performs.
Does the Agent Self-Reflection Sentiment Scanner work with Claude or Cursor?
Yes, you can connect this MCP to any compatible client like Claude, Cursor, or Windsurf to analyze your agent's performance directly in your workflow.
How does the Agent Self-Reflection Sentiment Scanner measure agent stability?
It measures stability by looking at the relationship between error recognition and successful task completion within the execution logs.
What are self-correction markers?
They are specific linguistic phrases like 'I made a mistake' or 'The task is complete' that indicate an agent's internal state transition.
How is the stability score calculated?
The stability score is derived from the frequency rate of error recognition markers relative to the total number of completed execution loops.
Can I use this with Claude Desktop?
Yes, this MCP server can be connected to Claude Desktop, Cursor, VS Code, Windsurf, and any other MCP-compatible client via Vinkius Edge.
One connection away
Give your agent a direct line to Agent Self-Reflection Sentiment Scanner.
Connect Agent Self-Reflection Sentiment Scanner once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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