Tokenization Normalizer Connector for AI agents.
3 live capabilities
Fix tokenization drift for predictable model behavior
Waiting for input…
Why people use Tokenization Normalizer
Stop prompt instability with Tokenization Normalizer
This MCP changes that by giving you a way to force your text into a predictable shape. Instead of guessing, you align your input to the exact structural rules the model uses. You get consistent, repeatable results that don't break when the model's tokenizer sees a slightly different character.
What Vinkius changes
You get text that is perfectly formatted for the specific model you are targeting.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Fixing broken prompts after a model update
An engineer notices a prompt works on one model but fails on another.
- Real-world use case 02
Standardizing data for batch processing
A researcher needs to run 1,000 prompts through a specific model.
- Real-world use case 03
Debugging agentic reasoning errors
An agent starts hallucinating due to weird character encoding.
Complete set · 3capabilities
The complete Tokenization Normalizer capability set.
These are the exact actions your AI can choose when you ask it to work with Tokenization Normalizer.
01—03
3 capabilities in this set.
Part of 3 available through Tokenization Normalizer.
- 01 Capability
Analyze tokenization drift
Compares your text against a target profile to see how much it deviates. It provides a summary of how unstable the text is for that specific model.
- 02 Capability
Normalize text structure
Transforms your string to match the structural expectations of a specific tokenizer. It fixes whitespace and character issues to match the target profile.
- 03 Capability
Get tokenizer capabilities
Lists the specific normalization rules available for a chosen profile. It tells you exactly what the tokenizer can handle.
Set up in minutes
One URL. Then ask Tokenization Normalizer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Tokenization Normalizer 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_o1uQr4UTZ6TKL3SVo9yFjltzxiK6QVA7RZ6eze6b/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 Tokenization Normalizer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Tokenization Normalizer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_o1uQr4UTZ6TKL3SVo9yFjltzxiK6QVA7RZ6eze6b/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 Tokenization Normalizer URL.
- Step 03
Save and start
Save the connection and enable Tokenization Normalizer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"tokenization-normalizer": {
"url": "https://edge.vinkius.com/vk_preview_o1uQr4UTZ6TKL3SVo9yFjltzxiK6QVA7RZ6eze6b/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 Tokenization Normalizer
Open Agent mode in chat and ask: "Using Tokenization Normalizer, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"tokenization-normalizer": {
"url": "https://edge.vinkius.com/vk_preview_o1uQr4UTZ6TKL3SVo9yFjltzxiK6QVA7RZ6eze6b/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 Tokenization Normalizer
Ask Copilot: "Using Tokenization Normalizer, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"tokenization-normalizer": {
"url": "https://edge.vinkius.com/vk_preview_o1uQr4UTZ6TKL3SVo9yFjltzxiK6QVA7RZ6eze6b/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 Tokenization Normalizer
Open Cascade and ask: "Using Tokenization Normalizer, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"tokenization-normalizer": {
"url": "https://edge.vinkius.com/vk_preview_o1uQr4UTZ6TKL3SVo9yFjltzxiK6QVA7RZ6eze6b/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 Tokenization Normalizer
Ask Cline: "Using Tokenization Normalizer, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add tokenization-normalizer --transport http "https://edge.vinkius.com/vk_preview_o1uQr4UTZ6TKL3SVo9yFjltzxiK6QVA7RZ6eze6b/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 Tokenization Normalizer
Ask Claude: "Using Tokenization Normalizer, show me...". 3 tools are ready
Where the request belongs
Work Tokenization Normalizer can move forward.
This is for engineers and researchers who need their prompts to behave predictably across different model architectures.
Prompt Engineer
Ensures that complex instructions don't break due to subtle whitespace or character shifts.
LLM Researcher
Maintains strict control over input data to ensure experimental results are reproducible.
AI Agent Developer
Builds reliable automated workflows where prompt stability is a requirement for success.
Build the capability set
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Bring your own AI
Change the model, client or framework. Keep Tokenization Normalizer connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
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Before you connect
Questions about Tokenization Normalizer.
The practical details behind the request, access and result.
How can Tokenization Normalizer help my AI agent's accuracy?
It ensures your instructions are read exactly how you intended by fixing formatting issues that cause models to misinterpret prompts.
Can I use Tokenization Normalizer with Claude or Cursor?
Yes, as long as your client is MCP-compatible, you can use this to clean your prompts before they are sent to the model.
Does Tokenization Normalizer work for different model types?
Yes, it includes specific profiles for various architectures, allowing you to tailor text for different model families.
What is tokenization drift and how does this MCP fix it?
Drift happens when text is formatted in a way the model doesn't expect. This MCP detects that mismatch and reshapes your text to match the model's rules.
Is Tokenization Normalizer useful for production AI workflows?
Absolutely. It provides the deterministic control needed to ensure prompts behave the same way in production as they did during testing.
What is tokenization drift?
Tokenization drift occurs when the same text results in different token counts or boundaries across different LLM models. This capability helps align text to a target profile to minimize these differences.
Which tokenizers are supported?
The server supports various profiles including cl100k_base, p50k_base, and llama. You can use get_tokenizer_capabilities to see the full list of supported rules for each.
How do I use this with Cursor or Claude Desktop?
Connect via Vinkius Edge using your personal Connection Token. Once connected, any MCP-compatible client like Cursor, VS Code, Claude Desktop, or Windsurf can use the capabilities.
One connection away
Give your agent a direct line to Tokenization Normalizer.
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