Memory Context Window Pruner Connector for AI agents.
3 live capabilities
Manage conversation token limits and prevent context overflow
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Why people use Memory Context Window Pruner
Prevent context overflow with Memory Context Window Pruner
With this MCP, you stop guessing how much space you have left. You can see the exact token count and then surgically trim the history so your agent stays sharp and stays within its limits.
What Vinkius changes
That this MCP keeps your AI agent from hitting context limits by intelligently thinning out old chat data.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Long-form coding sessions
When a coding chat gets too long and the agent starts forgetting the initial project requirements, use prune_history to clear the clutter while keeping the core logic.
- Real-world use case 02
Managing token costs
An engineer uses estimate_token_usage to monitor a massive chat history and decides to prune it before the next prompt becomes too expensive.
- Real-world use case 03
Maintaining agent focus
A user applies a relevance-weighted strategy to ensure the agent remembers the specific constraints of a complex task even after hundreds of messages.
Complete set · 3capabilities
The complete Memory Context Window Pruner capability set.
These are the exact actions your AI can choose when you ask it to work with Memory Context Window Pruner.
01—03
3 capabilities in this set.
Part of 3 available through Memory Context Window Pruner.
- 01 Capability
Estimate token usage
Calculates the total token count of your current message history. This helps you see exactly how close you are to the context limit.
- 02 Capability
Prune history
Shrinks your conversation history using specific rules like keeping the last N messages. It's the primary way to clear out old data.
- 03 Capability
Validate strategy constraints
Tests your pruning plan to make sure it's logically sound. It prevents you from running a strategy that won't actually save any space.
Set up in minutes
One URL. Then ask Memory Context Window Pruner to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Memory Context Window Pruner 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_7axJrUJdLscsn1NJUQ6AtdZlF7EKsqrmpd776fMB/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 Memory Context Window Pruner, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Memory Context Window Pruner for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_7axJrUJdLscsn1NJUQ6AtdZlF7EKsqrmpd776fMB/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 Memory Context Window Pruner URL.
- Step 03
Save and start
Save the connection and enable Memory Context Window Pruner in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-window-pruner": {
"url": "https://edge.vinkius.com/vk_preview_7axJrUJdLscsn1NJUQ6AtdZlF7EKsqrmpd776fMB/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 Memory Context Window Pruner
Open Agent mode in chat and ask: "Using Memory Context Window Pruner, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-window-pruner": {
"url": "https://edge.vinkius.com/vk_preview_7axJrUJdLscsn1NJUQ6AtdZlF7EKsqrmpd776fMB/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 Memory Context Window Pruner
Ask Copilot: "Using Memory Context Window Pruner, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-window-pruner": {
"url": "https://edge.vinkius.com/vk_preview_7axJrUJdLscsn1NJUQ6AtdZlF7EKsqrmpd776fMB/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 Memory Context Window Pruner
Open Cascade and ask: "Using Memory Context Window Pruner, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-window-pruner": {
"url": "https://edge.vinkius.com/vk_preview_7axJrUJdLscsn1NJUQ6AtdZlF7EKsqrmpd776fMB/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 Memory Context Window Pruner
Ask Cline: "Using Memory Context Window Pruner, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add memory-context-window-pruner --transport http "https://edge.vinkius.com/vk_preview_7axJrUJdLscsn1NJUQ6AtdZlF7EKsqrmpd776fMB/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 Memory Context Window Pruner
Ask Claude: "Using Memory Context Window Pruner, show me...". 3 tools are ready
Where the request belongs
Work Memory Context Window Pruner can move forward.
This is for developers and power users building long-form AI applications or managing massive, multi-turn conversations that would otherwise crash or become too expensive.
AI Engineer
Managing token budgets and context window stability for production-grade agents.
Prompt Engineer
Optimizing long-running chat sessions to ensure the agent doesn't lose track of instructions.
Data Scientist
Handling large datasets through conversational interfaces without hitting memory ceilings.
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Bring your own AI
Change the model, client or framework. Keep Memory Context Window Pruner connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
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Cline -
Zed -
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Before you connect
Questions about Memory Context Window Pruner.
The practical details behind the request, access and result.
How can I stop my AI agent from forgetting things in long chats?
You can use the Memory Context Window Pruner to intelligently trim old, irrelevant messages while keeping the most important context and recent instructions active.
Will using the Memory Context Window Pruner make my chats shorter?
Yes, it reduces the total number of tokens in your conversation history, which keeps the chat size manageable and prevents errors.
Can I check my token usage before I prune my history?
Yes, you can check the exact token impact of your current conversation before deciding to apply any pruning strategies.
How does this MCP prevent AI errors during long sessions?
It prevents 'context overflow' errors by ensuring the conversation never exceeds the maximum capacity of your AI client's memory.
Is it safe to prune my conversation history?
It is safe as long as you use the right strategy. You can verify your plan first to make sure you aren't deleting something vital to the task.
What is the purpose of this MCP?
It helps prevent LLM context window overflow by providing deterministic ways to prune message history.
How do I know how many tokens I am using?
You can use the estimate_token_usage capability to calculate the cumulative token impact of your message list.
Can I validate my pruning strategy before applying it?
Yes, the validate_strategy_constraints capability allows you to check if a pruning configuration is logically sound.
One connection away
Give your agent a direct line to Memory Context Window Pruner.
Connect Memory Context Window Pruner once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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