LLM Token Counter Connector for AI agents.
2 live capabilities
Manage context windows and prevent token overflow for LLM deployments.
Waiting for input…
Why people use LLM Token Counter
LLM Token Counter for precise context window management
With this Connector, you get the actual numbers for different encodings like cl100k_base and o200k_base. It helps you see how much your chat templates are adding to the overhead and where you should actually cut off text when things get too long. You get to spend less time guessing and more time building.
What Vinkius changes
You get precise data to keep your AI applications within their limits.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Trimming long conversation history
A developer needs to trim a long conversation history without losing the core context.
- Real-world use case 02
Calculating chat template overhead
An engineer wants to see how much a specific chat template adds to the total token count.
- Real-world use case 03
Evaluating prompt complexity
A prompt designer wants to check if a new system instruction is too complex for the model.
Complete set · 2capabilities
The complete LLM Token Counter capability set.
These are the exact actions your AI can choose when you ask it to work with LLM Token Counter.
01—02
2 capabilities in this set.
Part of 2 available through LLM Token Counter.
- 01 Capability
Analyze complexity
Check text complexity and punctuation diversity. This helps you see how text is structured and identify patterns.
- 02 Capability
Token count
Calculate character, word, and estimated token counts. It provides precise numbers for different model encodings.
Set up in minutes
One URL. Then ask LLM Token Counter to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LLM Token Counter 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_zsoth4tZ4xpY6SYfMN3vLiFJZi46jUAy7QtwPENf/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 LLM Token Counter, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable LLM Token Counter for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_zsoth4tZ4xpY6SYfMN3vLiFJZi46jUAy7QtwPENf/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 LLM Token Counter URL.
- Step 03
Save and start
Save the connection and enable LLM Token Counter in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"llm-token-counter": {
"url": "https://edge.vinkius.com/vk_preview_zsoth4tZ4xpY6SYfMN3vLiFJZi46jUAy7QtwPENf/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 LLM Token Counter
Open Agent mode in chat and ask: "Using LLM Token Counter, help me...". 2 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"llm-token-counter": {
"url": "https://edge.vinkius.com/vk_preview_zsoth4tZ4xpY6SYfMN3vLiFJZi46jUAy7QtwPENf/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 LLM Token Counter
Ask Copilot: "Using LLM Token Counter, help me...". 2 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"llm-token-counter": {
"url": "https://edge.vinkius.com/vk_preview_zsoth4tZ4xpY6SYfMN3vLiFJZi46jUAy7QtwPENf/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 LLM Token Counter
Open Cascade and ask: "Using LLM Token Counter, help me...". 2 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"llm-token-counter": {
"url": "https://edge.vinkius.com/vk_preview_zsoth4tZ4xpY6SYfMN3vLiFJZi46jUAy7QtwPENf/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 LLM Token Counter
Ask Cline: "Using LLM Token Counter, help me...". 2 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add llm-token-counter --transport http "https://edge.vinkius.com/vk_preview_zsoth4tZ4xpY6SYfMN3vLiFJZi46jUAy7QtwPENf/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 LLM Token Counter
Ask Claude: "Using LLM Token Counter, show me...". 2 tools are ready
Where the request belongs
Work LLM Token Counter can move forward.
The LLM Token Counter is for developers who need to stay under context limits while maintaining high-quality responses.
LLM Engineer
Testing prompt limits and calculating overhead for production deployment.
AI Developer
Debugging context window issues and finding optimal truncation points.
Prompt Engineer
Analyzing text complexity to ensure prompts stay clear and efficient.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsAI Token Counter
Give AI Agents self-awareness of context limits. Count exact OpenAI/Claude tokens local to prevent fatal API truncation errors.
LLM Context Window Budgeter
Monitor and predict LLM context window exhaustion with precision token forecasting.
Anthropic
Access Claude models via Anthropic API. send messages, count tokens, manage batches and discover models from any AI agent.
String Metrics Analyzer
Bypass LLM tokenization blindness. Get exact character counts, word counts, and specific string occurrences deterministically.
LLM API Cost Calculator
Estimate and compare the financial impact of LLM usage across different providers.
Keywords AI
Monitor and optimize your LLM API usage with a unified gateway that tracks costs, latency, and model performance across providers.
Bring your own AI
Change the model, client or framework. Keep LLM Token Counter 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 LLM Token Counter.
The practical details behind the request, access and result.
How does the LLM Token Counter help me save money?
It gives you a precise look at how many tokens your prompts actually use. By knowing the exact count, you can trim unnecessary text and avoid paying for tokens that don't contribute to the output.
Can I use the LLM Token Counter for different types of models?
Yes, it supports multiple encodings like cl100k_base and o200k_base. This ensures you get accurate counts regardless of which model you are currently targeting.
What is a context window and why do I need to count tokens?
A context window is the limit on how much information a model can process at once. If you exceed it, the model will fail or forget earlier parts of the conversation. This capability helps you stay under that limit.
How does the LLM Token Counter handle long pieces of text?
It breaks down the text to provide character, word, and estimated token counts. It also analyzes the complexity to help you find the best place to truncate the content.
Will the LLM Token Counter tell me if my prompt is too big?
It provides the exact count so you can compare it against your model's limits. It also helps you identify which parts of your prompt are contributing most to the total count.
Which LLM tokenizers are supported?
The server provides exact counts for cl100k_base (GPT-4) and o200k_base (GPT-4o), as well as approximations for Claude and SentencePiece-based models like Llama.
How does the capability handle chat message overhead?
The calculate_chat_overhead logic accounts for the hidden structural tokens (like role indicators and delimiters) added by API templates to ensure your total token count is accurate.
Can I use this for text truncation?
Yes, you can use the find_truncation_point capability to determine exactly where to trim your input text to stay within a specific token budget.
One connection away
Give your agent a direct line to LLM Token Counter.
Connect LLM Token Counter once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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