Reasoning Step Word Count Analyzer Connector for AI agents.
3 live capabilities
Audit ReAct trace verbosity and catch zero-<0xA0>shot reasoning behavior.
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Why people use Reasoning Step Word Count Analyzer
Stop guessing why Reasoning Step Word Count Analyzer traces are failing
This MCP automates the entire audit. It parses the traces and flags exactly where the reasoning broke down. You get clear metrics on verbosity and zero-shot detection, so you can fix your prompts immediately.
What Vinkius changes
You stop guessing if your agent is actually thinking or just reacting.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Detecting sudden reasoning drops
An engineer notices an agent is failing tasks and uses the MCP to find that it's skipping thoughts entirely.
- Real-world use case 02
Optimizing token costs
A developer uses the verbosity capability to prune unnecessary words from long-winded thought blocks.
- Real-world use case 03
Debugging ReAct logic
You feed a broken trace into the capability to see if the parser can still extract the action and thought segments.
Complete set · 3capabilities
The complete Reasoning Step Word Count Analyzer capability set.
These are the exact actions your AI can choose when you ask it to work with Reasoning Step Word Count Analyzer.
01—03
3 capabilities in this set.
Part of 3 available through Reasoning Step Word Count Analyzer.
- 01 Capability
Calculate verbosity ratio
Compares the amount of text in thought blocks against action blocks. It helps you see if your agent is being too brief or overly wordy.
- 02 Capability
Parse react trace
Breaks down a raw ReAct string into structured parts. It makes it easy to analyze individual segments of a trace.
- 03 Capability
Detect zero shot behavior
Flags instances where the model skips reasoning by checking for very short thought blocks. This is great for catching sudden drops in reasoning quality.
Set up in minutes
One URL. Then ask Reasoning Step Word Count Analyzer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Reasoning Step Word Count Analyzer 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_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/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 Reasoning Step Word Count Analyzer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Reasoning Step Word Count Analyzer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/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 Reasoning Step Word Count Analyzer URL.
- Step 03
Save and start
Save the connection and enable Reasoning Step Word Count Analyzer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"reasoning-step-word-count-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/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 Reasoning Step Word Count Analyzer
Open Agent mode in chat and ask: "Using Reasoning Step Word Count Analyzer, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"reasoning-step-word-count-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/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 Reasoning Step Word Count Analyzer
Ask Copilot: "Using Reasoning Step Word Count Analyzer, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"reasoning-step-word-count-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/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 Reasoning Step Word Count Analyzer
Open Cascade and ask: "Using Reasoning Step Word Count Analyzer, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"reasoning-step-word-count-analyzer": {
"url": "https://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/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 Reasoning Step Word Count Analyzer
Ask Cline: "Using Reasoning Step Word Count Analyzer, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add reasoning-step-word-count-analyzer --transport http "https://edge.vinkius.com/vk_preview_ekv3hS0trxu4SmkQGQPI5KigM6tgHZpfvfQmz7Gj/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 Reasoning Step Word Count Analyzer
Ask Claude: "Using Reasoning Step Word Count Analyzer, show me...". 3 tools are ready
Where the request belongs
Work Reasoning Step Word Count Analyzer can move forward.
The AI engineer who's tired of manually scrolling through thousands of lines of logs to find where a chain-of-thought broke down.
AI Engineer
Debugging ReAct logic and reasoning quality in production agents.
LLM Developer
Monitoring token usage and reasoning consistency during testing cycles.
Prompt Engineer
Refining prompts to ensure the model follows a structured thought/action loop.
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Bring your own AI
Change the model, client or framework. Keep Reasoning Step Word Count Analyzer connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
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Before you connect
Questions about Reasoning Step Word Count Analyzer.
The practical details behind the request, access and result.
How does Reasoning Step Word Count Analyzer help with agent debugging?
It identifies when your agent is skipping the reasoning phase or being too wordy, making it easier to fix ReAct prompts.
Can I use Reasoning Step Word Count Analyzer to save tokens?
Yes, by using the verbosity ratio capability, you can find and prune unnecessary text in your agent's thought blocks.
Does Reasoning Step Word Count Analyzer work with any AI client?
It works with any MCP-compatible client like Claude, Cursor, or Windsurf that can connect to the Vinkius catalog.
What is zero-shot behavior in ReAct traces?
It's when your agent jumps straight to an action without a proper thought process. This MCP flags those instances for you.
Can Reasoning Step Word Count Analyzer parse messy logs?
Yes, it can take raw ReAct strings and turn them into structured data that is much easier to analyze.
How does the analyzer detect zero-shot behavior?
The detect_zero_shot_behavior capability analyzes the word count of a thought block. If the count is below the threshold, it flags the trace as likely zero-shot behavior.
Can I use this to compare different model traces?
Yes. By using calculate_verbosity_ratio, you can quantitatively compare the reasoning depth of different models by examining their thought-to-action text ratios.
What input format is required for parsing?
The parse_react_trace capability accepts a raw string containing the ReAct trace. It uses exact whitespace splitting to extract structured components.
One connection away
Give your agent a direct line to Reasoning Step Word Count Analyzer.
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