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Vinkius

GPT Tokenizer Connector for AI agents.

1 live capability

Manage context limits and prevent truncated responses in your RAG pipelines.

Live agent request GPT Tokenizer / Connector

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AI Agent

Why people use GPT Tokenizer

AI Token Counter: Stop Context Window Crashes in RAG Pipelines

This Connector gives your agent a pair of glasses. Instead of blindly shoving data into the prompt, your agent can check the size first. It sees the exact token count and can then decide to summarize, skip, or chunk the data on the fly. You stop playing whack-a-mole with your context limits and start building pipelines that actually work.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a way to stop your AI from guessing about context limits.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 6,100+ Connectors

  1. Real-world use case 01

    RAG Document Summarization

    An agent pulls 15 documents.

  2. Real-world use case 02

    JSON Data Processing

    You have a massive JSON file.

  3. Real-world use case 03

    Transcript Analysis

    You're processing a 2-hour meeting.

Complete set · 1capability

The complete GPT Tokenizer capability set.

These are the exact actions your AI can choose when you ask it to work with GPT Tokenizer.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through GPT Tokenizer.

  1. 01 Capability

    Count tokens

    Send a block of text to get the exact count using the cl100k_base encoding. This lets you check if your data fits before you try to send it.

Set up in minutes

One URL. Then ask GPT Tokenizer to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use GPT Tokenizer from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_3TlaUrJfXkjzPylHQtgw7qX3SzCKmj8umGecyFc1/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it GPT Tokenizer, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable GPT Tokenizer for the conversation.

Where the request belongs

Work GPT Tokenizer can move forward.

Built around the request

This is for the AI engineer building RAG pipelines who is tired of their agents hitting context window exceeded errors at the worst possible moment. It's for the developer who needs to keep costs down by knowing exactly how much data is being sent.

01

AI Engineer

Building RAG systems and needing to handle large document retrieval without crashing the LLM.

02

LLM Developer

Creating automated agents that process long transcripts or huge JSON files.

03

Prompt Engineer

Trying to maximize the information density of every single request to save on costs.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
LLM Context Window Budgeter logo
01 Managed Connector

LLM Context Window Budgeter

Monitor and predict LLM context window exhaustion with precision token forecasting.

View Connector
LLM Token Counter logo
02 2 capabilities

LLM Token Counter

Calculate exact and estimated token counts for GPT-4, GPT-4o, Claude, and Llama models.

View Connector
Anthropic logo
03 6 capabilities

Anthropic

Access Claude models via Anthropic API. send messages, count tokens, manage batches and discover models from any AI agent.

View Connector
Keywords AI logo
04 11 capabilities

Keywords AI

Monitor and optimize your LLM API usage with a unified gateway that tracks costs, latency, and model performance across providers.

View Connector
Memory Context Priority Pruner logo
05 3 capabilities

Memory Context Priority Pruner

Deterministic context window management by prioritizing essential and high-relevance messages.

View Connector
Portkey logo
06 10 capabilities

Portkey

AI gateway observability: monitor logs, costs, and manage LLM configurations via agents.

View Connector

Bring your own AI

Change the model, client or framework. Keep GPT Tokenizer connected.

  • 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 GPT Tokenizer.

The practical details behind the request, access and result.

What does the AI Token Counter MCP do?

It gives your AI agent the ability to see exactly how many tokens are in a piece of text before it sends it to a model. This helps prevent errors caused by exceeding the model's limits.

How does this help with RAG systems?

It prevents your RAG pipeline from crashing when it retrieves too much data. Your agent can check the size of the documents it found and decide how to summarize them safely.

Does the AI Token Counter work for both OpenAI and Claude?

Yes, it uses the cl100k_base encoding standard, which is the same one used by major models like GPT-4 and Claude.

Why can't I just count the words instead?

Models don't see words; they see tokens. A word can be one token or several. Using this Connector ensures you get the exact number the model will see, not just a word count.

How does this save me money?

By knowing the exact token count before you send a request, your agent can trim unnecessary data, ensuring you don't pay for more tokens than you actually need.

Does it work offline?

Yes, the token counting happens locally on your machine. You don't need to make any extra API calls to get the count.

How do I stop my agent from crashing?

Connect this Connector to your agent. It will then be able to check the size of its own data and automatically handle chunks that are too large for the context window.

What tokenizer algorithm is used?

It uses the cl100k_base encoding, which is the exact algorithm used by GPT-3.5, GPT-4, and most Claude models.

Does it send my text to OpenAI?

No. The calculation happens 100% local within the Edge engine using mathematical mapping.

Is it safe for large texts?

Yes, it evaluates the exact token structure rapidly. But keep in mind standard Edge memory limits (under 10MB per payload).

One connection away

Give your agent a direct line to GPT Tokenizer.

Connect GPT Tokenizer once. Keep it beside 6,100+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available