- PREVENT
- REDUCE
- CLEAN
- HUMAN
Short answer
How do I stop my AI from hitting context window errors?
Your AI uses tokenization to calculate the exact length of your text before it sends it to the model. This prevents the model from cutting off your data or throwing errors. You get a clear view of how much space your input takes up.
The outcomes of token monitoring.
Where your data lands.
Running this task gives you three ways to handle your data.
PREVENT
Avoid context errors
The AI calculates the token count of your input. This stops the model from failing when you send too much text at once.
REDUCE
Lower your costs
By knowing exactly how many tokens you use, your agent can trim unnecessary parts of a prompt. This keeps your usage efficient.
CLEAN
Format text properly
The AI converts raw text into tokens and back again. This ensures the data structure stays clean for the model.
HUMAN
Manual trimming
If the count is too high, the AI tells you. You then decide which parts of the text to delete or shorten yourself.
The workflow
What your AI does when text arrives.
The AI handles the math so you don't have to guess how much text you can send.
Count tokens
Your agent breaks your text down into individual tokens to see the actual size. This is the first step in preventing errors.
tokenizeCheck limits
The AI compares the token count against the model's maximum capacity. It identifies if the text is too large.
tokenizeReconstruct text
If you need to see how the tokens look as a single string, the AI converts them back. This helps you verify the content.
detokenizeReport usage
The AI provides the final count to you. You use this number to decide if you need to edit your prompt.
tokenizedetokenize
Try it
Copy these to start.
Use these prompts to begin managing your token counts.
Starting points
These are basic ways to start. Replace the bracketed text with your actual content.
Cohere Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcpClaude Desktop
Follow the steps below to connect in seconds.
- 1In Claude Desktop, open Settings → Connectors.
- 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
- 3Click Add and start a new chat — Cohere capabilities are ready to use.
{
"mcpServers": {
"cohere-mcp": {
"url": "https://edge.vinkius.com/vk_preview_R4ERIik35jE1vo5hRbvNHBrwGzLbWOtl3sZQNmPU/mcp"
}
}
}Tokenize this document from my local folder so I can see the count.
Check the token count for this long email draft before I send it.
How many tokens are in this technical documentation snippet?
Convert these tokens back into text to check for formatting errors.
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Start here
Connect Cohere once, then ask.
Just link your account once. Your credentials stay encrypted and your AI client gets immediate access to these tools.
Connect Cohere to your AIFAQ
How this task behaves.
- 01
Can the AI automatically delete text to fit the limit?
No. The AI only reports the token count. You must decide what to remove or change yourself.
- 02
Does this tool change my original files?
No. The tools only read the text to count tokens. They do not modify your documents or files.
- 03
How does the AI know the token count?
It uses the tokenize tool to break your text into the specific units the model understands.
- 04
Can I use this to reduce my API costs?
Yes, by checking the count first, you can trim your prompts to use fewer tokens before sending them.
- 05
Will this work with any AI client?
Yes, it works with any MCP-compatible client like Claude, Cursor, or Windsurf.
More questions about Cohere? See everything the Cohere Connector can do
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