Memory Context Priority Pruner Connector for AI agents.
3 live capabilities
Keep your agent focused by managing context window limits.
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Why people use Memory Context Priority Pruner
Memory Context Priority Pruner for context window management
This Connector automates that cleanup. It identifies what to keep and what to cut, so you just set a limit and let it work.
What Vinkius changes
You keep your most important context without hitting window limits.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Long-running coding sessions
When a chat gets too large, use the pruner to clear out old logs while keeping the current task instructions intact.
- Real-world use case 02
Complex research tasks
Keep your core research goals in view even after hours of deep diving into different topics and datasets.
- Real-world use case 03
Agentic workflow stability
Prevent agents from hallucinating due to context overflow by maintaining a clean, relevant message history.
Complete set · 3capabilities
The complete Memory Context Priority Pruner capability set.
These are the exact actions your AI can choose when you ask it to work with Memory Context Priority Pruner.
01—03
3 capabilities in this set.
Part of 3 available through Memory Context Priority Pruner.
- 01 Capability
Predict pruning impact
You can simulate a pruning strategy before it happens. This shows you exactly which messages might be lost.
- 02 Capability
Analyze context density
This checks how tokens are spread across your message history. It helps you see where the bulk of your usage is coming from.
- 03 Capability
Execute context reduction
This performs the actual removal of messages to fit within your budget. It ensures you stay under your token limit.
Set up in minutes
One URL. Then ask Memory Context Priority Pruner to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Memory Context Priority 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_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/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 Priority Pruner, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Memory Context Priority Pruner for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/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 Priority Pruner URL.
- Step 03
Save and start
Save the connection and enable Memory Context Priority Pruner in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-priority-pruner": {
"url": "https://edge.vinkius.com/vk_preview_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/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 Priority Pruner
Open Agent mode in chat and ask: "Using Memory Context Priority Pruner, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-priority-pruner": {
"url": "https://edge.vinkius.com/vk_preview_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/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 Priority Pruner
Ask Copilot: "Using Memory Context Priority Pruner, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-priority-pruner": {
"url": "https://edge.vinkius.com/vk_preview_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/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 Priority Pruner
Open Cascade and ask: "Using Memory Context Priority Pruner, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"memory-context-priority-pruner": {
"url": "https://edge.vinkius.com/vk_preview_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/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 Priority Pruner
Ask Cline: "Using Memory Context Priority Pruner, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add memory-context-priority-pruner --transport http "https://edge.vinkius.com/vk_preview_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/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 Priority Pruner
Ask Claude: "Using Memory Context Priority Pruner, show me...". 3 tools are ready
Where the request belongs
Work Memory Context Priority Pruner can move forward.
This is for developers and engineers managing long-running, high-token agentic workflows that frequently hit context boundaries.
AI Engineer
Managing complex, multi-turn agent loops that require stable memory.
Prompt Engineer
Testing massive instruction sets that need a clean history to remain effective.
LLM Developer
Building applications where token cost and window limits are critical constraints.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Context Engineering Prover
An AI dumped 80,000 tokens into a prompt. 64,000 of them unreferenced noise. It said 'best practice' to justify the structure and 'looks good' to measure quality. That is not context engineering. that is a copy-paste pipeline. This capability forces five context axes: relevance auditing, priority structuring, token budgeting, evidence grounding, and quality measurement.
Keywords AI
Monitor and optimize your LLM API usage with a unified gateway that tracks costs, latency, and model performance across providers.
Portkey
AI gateway observability: monitor logs, costs, and manage LLM configurations via agents.
Bring your own AI
Change the model, client or framework. Keep Memory Context Priority Pruner 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 Memory Context Priority Pruner.
The practical details behind the request, access and result.
How does Memory Context Priority Pruner stop context overflow?
It uses deterministic rules to remove low-relevance messages while protecting your most important instructions and recent history.
Can I use Memory Context Proximity Pruner with Claude or Cursor?
Yes, it works with any MCP-compatible client like Claude, Cursor, Windsurf, or VS Code.
Will Memory Context Priority Pruner delete my system instructions?
No, the capability is designed to keep your system prompts and initial messages immutable and safe from pruning.
How do I know if my agent is running out of space?
You can check your current token distribution to see how much of your window is occupied by different message tiers.
Is there a way to test pruning before it happens?
Yes, you can simulate a strategy to see exactly which messages will be removed and how much space you will save.
What makes the pruning process deterministic?
The process follows strict, unchangeable rules: it always preserves system messages and the first user message, protects a recent buffer of N messages, and then selects middle messages based on their relevance score until the token budget is reached.
How can I see how much space my current conversation is using?
You can use the analyze_context_density capability. It will break down your message list into immutable, buffer, and prunable token counts.
Can I simulate a pruning run before actually deleting messages?
Yes, the predict_pruning_impact capability allows you to estimate how many tokens will be saved and what information will remain without modifying your actual message list.
One connection away
Give your agent a direct line to Memory Context Priority Pruner.
Connect Memory Context Priority Pruner once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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