Chain-of-Thought Skeleton Verifier Connector for AI agents.
3 live capabilities
Validate agent reasoning structures and parsing patterns
Waiting for input…
Why people use Chain-of-Thought Skeleton Verifier
Stop broken XML tags with Chain-of_Thought Skeleton Verifier
This MCP automates that entire audit process. You get immediate feedback on whether your tags match and if your action/observation loops are intact.
What Vinkius changes
You stop guessing why your parser failed and start seeing exactly where the logic broke.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Broken XML tags in production
An agent fails to parse because a tag was not closed.
- Real-world use case 02
Inconsistent prefixing
You switched from XML to keyword prefixes and need to verify all outputs follow the new rule using verify_parsing_pattern.
- Real-world use case 03
Measuring agent efficiency
You want to know if your agents are over-thinking or being too brief by using get_reasoning_stats to track step counts.
Complete set · 3capabilities
The complete Chain-of-Thought Skeleton Verifier capability set.
These are the exact actions your AI can choose when you ask it to work with Chain-of-Thought Skeleton Verifier.
01—03
3 capabilities in this set.
Part of 3 available through Chain-of-Thought Skeleton Verifier.
- 01 Capability
Check structural integrity
Checks for matching XML tags and ensures every action is followed by an observation. This helps prevent broken loops in your parser.
- 02 Capability
Get reasoning stats
Calculates quantitative metrics like thought step counts and reasoning efficiency. Use this to measure the density of your agentic workflows.
- 03 Capability
Verify parsing pattern
Confirms if text follows specific XML or keyword-based prefix patterns. It ensures your outputs adhere to expected structural families.
Set up in minutes
One URL. Then ask Chain-of-Thought Skeleton Verifier to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Chain-of-Thought Skeleton Verifier 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_SX8YQK6VgxKp2lw0GPkgcOK1OaBkpL1WDfLIicKS/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 Chain-of-Thought Skeleton Verifier, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Chain-of-Thought Skeleton Verifier for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_SX8YQK6VgxKp2lw0GPkgcOK1OaBkpL1WDfLIicKS/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 Chain-of-Thought Skeleton Verifier URL.
- Step 03
Save and start
Save the connection and enable Chain-of-Thought Skeleton Verifier in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-of-thought-skeleton-verifier": {
"url": "https://edge.vinkius.com/vk_preview_SX8YQK6VgxKp2lw0GPkgcOK1OaBkpL1WDfLIicKS/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 Chain-of-Thought Skeleton Verifier
Open Agent mode in chat and ask: "Using Chain-of-Thought Skeleton Verifier, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-of-thought-skeleton-verifier": {
"url": "https://edge.vinkius.com/vk_preview_SX8YQK6VgxKp2lw0GPkgcOK1OaBkpL1WDfLIicKS/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 Chain-of-Thought Skeleton Verifier
Ask Copilot: "Using Chain-of-Thought Skeleton Verifier, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-of-thought-skeleton-verifier": {
"url": "https://edge.vinkius.com/vk_preview_SX8YQK6VgxKp2lw0GPkgcOK1OaBkpL1WDfLIicKS/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 Chain-of-Thought Skeleton Verifier
Open Cascade and ask: "Using Chain-of-Thought Skeleton Verifier, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-of-thought-skeleton-verifier": {
"url": "https://edge.vinkius.com/vk_preview_SX8YQK6VgxKp2lw0GPkgcOK1OaBkpL1WDfLIicKS/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 Chain-of-Thought Skeleton Verifier
Ask Cline: "Using Chain-of-Thought Skeleton Verifier, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add chain-of-thought-skeleton-verifier --transport http "https://edge.vinkius.com/vk_preview_SX8YQK6VgxKp2lw0GPkgcOK1OaBkpL1WDfLIicKS/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 Chain-of-Thought Skeleton Verifier
Ask Claude: "Using Chain-of-Thought Skeleton Verifier, show me...". 3 tools are ready
Where the request belongs
Work Chain-of-Thought Skeleton Verifier can move forward.
AI engineers and prompt developers who need to ensure their agentic workflows are structurally sound and parseable.
AI Engineer
Auditing ReAct loops for production-ready reliability.
Prompt Engineer
Validating that new instructions do not break XML parsing patterns.
LLM Ops Specialist
Monitoring reasoning density and efficiency across agent populations.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Chain-of-Thought Skeleton Verifier
Validates the structural integrity and parsing patterns of ReAct and Chain-of-Thought agent outputs.
Reasoning Step Word Count Analyzer
Analyze ReAct traces to audit reasoning verbosity and detect zero-shot behavior.
Chainlit
Empower your AI agents to audit chat threads, analyze model steps, and track LLM observability metrics securely.
DeepSeek
Access powerful open-weight language models for reasoning, code generation, and complex problem solving at competitive cost.
Aporia
Monitor AI models and validate LLM interactions with guardrails directly from your AI agent to ensure safety and observability.
Critical Thinking Prover
AI agents accept premises without questioning, analyze from one perspective, cherry-pick evidence, ignore consequences, and present uncertainty as certainty. This capability forces rigor: surface assumptions, apply competing frameworks, weigh counterevidence, trace ripple effects, bound confidence.
Bring your own AI
Change the model, client or framework. Keep Chain-of-Thought Skeleton Verifier 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 Chain-of-Thought Skeleton Verifier.
The practical details behind the request, access and result.
What does it mean if `check_structural_integrity` returns broken loops?
A broken loop indicates that an action segment was detected in the text, but it was not followed by a corresponding observation segment, meaning the agent's execution cycle was interrupted.
What does it mean if `check_structural_integrity` returns broken loops?
A broken loop indicates that an action segment was detected in the text, but it was not followed by a corresponding observation segment, meaning the agent's execution cycle was interrupted.
Can I use this to detect if an agent is using XML tags or keyword prefixes?
Yes, the verify_parsing_pattern capability specifically identifies whether the input text follows the XML-style tag family or the keyword-based prefix family.
Can I use this to detect if an agent is using XML tags or keyword prefixes?
Yes, the verify_parsing_pattern capability specifically identifies whether the input text follows the XML-style tag family or the keyword-based prefix family.
How is reasoning density calculated?
The get_reasoning_stats capability calculates efficiency by comparing the number of completed thought blocks to the number of action blocks, providing a qualitative score like 'High' or 'Low'.
How is reasoning density calculated?
The get_reasoning_stats capability calculates efficiency by comparing the number of completed thought blocks to the number of action blocks, providing a qualitative score like 'High' or 'Low'.
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