Agent Self-Reflection & Sentiment Scanner Connector for AI agents.
2 live capabilities
Monitor execution logs to track self-correction and sentiment.
Waiting for input…
Why people use Agent Self-Reflection & Sentiment Scanner
Agent Self-Reflection & Sentiment Scanner for agent observability
Move from a question to a useful Agent Self-Reflection & Sentiment Scanner result without breaking your flow.
What Vinkius changes
You get measurable visibility into how your agent thinks and recovers from errors.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Debugging infinite loops
An engineer sees an agent repeating a task and uses the MCP to confirm it is stuck in a correction cycle.
- Real-world use case 02
Quality auditing
A developer reviews logs from a week of runs to find where high levels of uncertainty appear in the reasoning.
- Real-world use case 03
Performance benchmarking
You compare two different prompts by checking which one has a lower self-correction rate.
Complete set · 2capabilities
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—02
2 capabilities in this set.
Part of 2 available through Agent Self-Reflection & Sentiment Scanner.
- 01 Capability
Analyze sentiment
Checks the emotional tone of log entries to see if the agent is struggling or succeeding. It helps you identify shifts in confidence during a task.
- 02 Capability
Detect reflection
Finds specific parts of a log where the agent expresses internal thoughts or reasoning. This makes it easy to audit the agent's decision-making process.
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_F2yFzx4CchXScg2i4EwfRYqrArVJ0UEEfstzE52Z/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_F2yFzx4CchXScg2i4EwfRYqrArVJ0UEEfstzE52Z/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": {
"url": "https://edge.vinkius.com/vk_preview_F2yFzx4CchXScg2i4EwfRYqrArVJ0UEEfstzE52Z/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...". 2 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-self-reflection-sentiment-scanner": {
"url": "https://edge.vinkius.com/vk_preview_F2yFzx4CchXScg2i4EwfRYqrArVJ0UEEfstzE52Z/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...". 2 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-self-reflection-sentiment-scanner": {
"url": "https://edge.vinkius.com/vk_preview_F2yFzx4CchXScg2i4EwfRYqrArVJ0UEEfstzE52Z/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...". 2 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-self-reflection-sentiment-scanner": {
"url": "https://edge.vinkius.com/vk_preview_F2yFzx4CchXScg2i4EwfRYqrArVJ0UEEfstzE52Z/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...". 2 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add agent-self-reflection-sentiment-scanner --transport http "https://edge.vinkius.com/vk_preview_F2yFzx4CchXScg2i4EwfRYqrArVJ0UEEfstzE52Z/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...". 2 tools are ready
Where the request belongs
Work Agent Self-Reflection & Sentiment Scanner can move forward.
This is for the engineers and researchers who are tired of manually parsing through thousands of lines of text to find where an autonomous loop went wrong.
AI Engineer
Monitoring the reliability and error recovery of autonomous loops in production.
LLM Ops Specialist
Tracking performance degradation and stability across different agent versions.
QA Tester
Identifying edge cases where agents fail to recognize their own mistakes.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Agent Self-Reflection & Sentiment Scanner
Quantify agent self-correction behavior and sentiment by scanning execution logs for specific error and success markers.
Reasoning Step Word Count Analyzer
Analyzes ReAct traces to measure reasoning depth and identify zero-shot behavior.
Multi-Agent Communication Protocol Validator
Analyze and verify the structural integrity of autonomous agent communication logs.
Review Response Template Scorer
Automated evaluation of business response quality, personalization, and tone alignment.
Chainlit
Empower your AI agents to audit chat threads, analyze model steps, and track LLM observability metrics securely.
Chattermill
Analyze customer feedback and sentiment via Chattermill. track AI-powered themes, monitor NPS and CSAT, and unify feedback from every channel directly from any AI agent.
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 -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Agent Self-Reflection & Sentiment Scanner.
The practical details behind the request, access and result.
How can I use Agent Self-Reflection & Sentiment Scanner to find errors?
It scans your logs for specific markers like 'I made a mistake' or 'error'. This helps you pinpoint exactly where the agent recognized a failure.
Can Agent Self-Reflection & Sentiment Scanner track agent progress?
Yes, it determines if the current state is 'Correcting' or 'Proceeding' based on the markers found in your execution history.
Does Agent Self-Reflection & Sentiment Scanner work with any logs?
It works with any text-based execution logs from your AI client, making it easy to monitor different types of autonomous workflows.
How does Agent Self-Reflection & Sentiment Scanner help with debugging?
It quantifies how often an agent loops on errors, giving you a measurable way to see if your prompts are actually working.
Can I monitor sentiment trends with Agent Self-Reflection & Sentiment Scanner?
Yes, you can track how the emotional tone of the logs changes over time to identify when an agent is struggling.
How does the scanner identify self-correction?
The scanner uses exact character-for-character comparison to find predefined error recognition phrases and success markers within your provided execution logs.
Can I use this to monitor multiple agents?
Yes, you can pass the raw log text from any agent execution loop into the capabilities to analyze patterns across different agents and sessions.
What is the difference between `analyze_sentiment` and `detect_reflection`?
analyze_sentiment evaluates whether text is positive, negative, or neutral, while detect_reflection specifically looks for markers of internal thought or self-awareness.
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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