Context Window Token Estimator Connector for AI agents.
3 live capabilities
Measure and manage token usage for LLM context windows
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Why people use Context Window Token Estimator
Stop context overflow with Context Window Token Estimator
With this MCP, you stop guessing. You can see exactly how much space your system instructions, few-shot examples, and retrieved documents are taking up. It turns a blind guessing game into a precise engineering task.
What Vinkius changes
You stop guessing how much data your agent can actually handle.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Preventing RAG bloat
A developer realizes their agent is hallucinating because the retrieved context is too large.
- Real-world use case 02
Optimizing few-shot prompts
A prompt engineer wants to add more examples to a prompt but is worried about the limit.
- Real-world use case 03
Model tier planning
An engineer is moving a workflow from a small model to a larger one.
Complete set · 3capabilities
The complete Context Window Token Estimator capability set.
These are the exact actions your AI can choose when you ask it to work with Context Window Token Estimator.
01—03
3 capabilities in this set.
Part of 3 available through Context Window Token Estimator.
- 01 Capability
Analyze context distribution
Breaks down a full payload into its parts to check if it fits within model capacity. It shows exactly how much space each component uses.
- 02 Capability
Estimate payload tokens
Calculates the token count for specific pieces of input. This helps you measure individual parts like system prompts or user messages.
- 03 Capability
Get limit tier info
Finds the nearest standard model capacity tier for a specific number of tokens. It helps you match your data to the right model limits.
Set up in minutes
One URL. Then ask Context Window Token Estimator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Context Window Token Estimator 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_JSrmRV3GM3NOBDbS6k7ZafkvGpk7uNiLmaM4sSsQ/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 Context Window Token Estimator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Context Window Token Estimator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_JSrmRV3GM3NOBDbS6k7ZafkvGpk7uNiLmaM4sSsQ/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 Context Window Token Estimator URL.
- Step 03
Save and start
Save the connection and enable Context Window Token Estimator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"context-window-token-estimator": {
"url": "https://edge.vinkius.com/vk_preview_JSrmRV3GM3NOBDbS6k7ZafkvGpk7uNiLmaM4sSsQ/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 Context Window Token Estimator
Open Agent mode in chat and ask: "Using Context Window Token Estimator, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"context-window-token-estimator": {
"url": "https://edge.vinkius.com/vk_preview_JSrmRV3GM3NOBDbS6k7ZafkvGpk7uNiLmaM4sSsQ/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 Context Window Token Estimator
Ask Copilot: "Using Context Window Token Estimator, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"context-window-token-estimator": {
"url": "https://edge.vinkius.com/vk_preview_JSrmRV3GM3NOBDbS6k7ZafkvGpk7uNiLmaM4sSsQ/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 Context Window Token Estimator
Open Cascade and ask: "Using Context Window Token Estimator, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"context-window-token-estimator": {
"url": "https://edge.vinkius.com/vk_preview_JSrmRV3GM3NOBDbS6k7ZafkvGpk7uNiLmaM4sSsQ/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 Context Window Token Estimator
Ask Cline: "Using Context Window Token Estimator, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add context-window-token-estimator --transport http "https://edge.vinkius.com/vk_preview_JSrmRV3GM3NOBDbS6k7ZafkvGpk7uNiLmaM4sSsQ/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 Context Window Token Estimator
Ask Claude: "Using Context Window Token Estimator, show me...". 3 tools are ready
Where the request belongs
Work Context Window Token Estimator can move forward.
This is for developers and prompt engineers who are tired of hitting context limits or seeing their agents lose focus because the RAG context is too bloated.
Prompt Engineer
Optimizing long system prompts and few-shot examples to ensure they don't crowd out the user query.
AI Developer
Testing RAG pipelines to see how many document chunks can be safely injected into a specific model's window.
LLM Ops Engineer
Monitoring token usage and managing costs by keeping payloads lean and efficient.
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Bring your own AI
Change the model, client or framework. Keep Context Window Token Estimator 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 -
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Amazon Q -
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BoltAI -
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Jan -
LM Studio -
AnythingLLM -
Open WebUI -
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Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Context Window Token Estimator.
The practical details behind the request, access and result.
How can I use Context Window Token Estimator to prevent errors?
You can use it to check if your total input payload fits within the specific limits of your model before you send the request, preventing mid-run crashes.
Does Context Window Token Estimator work with any AI client?
Yes, as long as your client is MCP-compatible, such as Claude, Cursor, or Windsurf, you can use this to manage your token counts.
Can I see how much space my RAG data takes up with Context Window Token Estimator?
Yes, you can use the distribution analysis to see exactly what percentage of your context window is being used by retrieved documents versus your system instructions.
How accurate is the token counting in Context Window Token Estimator?
It uses deterministic heuristics to provide precise estimates that reflect how your agent's input is actually structured and measured.
Can I check specific model limits using Context Window Token Estimator?
Yes, you can identify the closest standard capacity tier for any given token count to ensure your data matches your target model.
How are tokens calculated?
The server uses a combination of character-based density (roughly 4 characters per token) and word-boundary splitting to provide a deterministic estimate.
Can I check if my prompt will exceed the model limit?
Yes, by using analyze_context_distribution, you can compare your total token count against standard tiers like 8k, 16k, 32k, or 128k.
What components can be analyzed?
You can analyze the system prompt, few-shot examples, RAG context, and the user query to see the full distribution.
One connection away
Give your agent a direct line to Context Window Token Estimator.
Connect Context Window Token Estimator once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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