# Memory Context Priority Pruner MCP for AI Agents MCP

> Memory Context Priority Pruner manages your context window by prioritizing essential messages and pruning low-relevance history. It uses deterministic rules to protect system instructions and recent buffers while trimming middle-tier content to keep you within your token budget.

## Overview
- **Category:** ai-infrastructure
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_wn6QQ6MBDELDQuy390LTPIrEYUANWateQNUsl4sW/mcp
- **Tags:** context-window, token-optimization, deterministic-pruning, memory-management, llm-efficiency

## Description

You are deep in a long session with your agent, and suddenly it starts losing the plot because the context window is full. Instead of manually deleting old messages or starting over, this MCP handles the cleanup for you. It keeps your core instructions and recent history safe while intelligently removing less relevant parts of the conversation. You can even check how much space you are using before making any changes to ensure you do not lose critical data. Since it is part of the Vinkius catalog, you just connect it to your preferred client and let it manage the heavy lifting of token optimization.

## Tools

### 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.

### execute_context_reduction
This performs the actual removal of messages to fit within your budget. It ensures you stay under your token limit.

### predict_pruning_impact
You can simulate a pruning strategy before it happens. This shows you exactly which messages might be lost.

## Prompt Examples

**Prompt:** 
```
How much space is left in my current session?
```

**Response:** 
```
Your current usage is as follows:

| Tier | Tokens |
| :--- | :--- |
| Immutable (System) | 1,200 |
| Protected Buffer | 800 |
| Middle-tier Messages | 2,500 |
| **Total** | **4,500**
```

**Prompt:** 
```
What happens if I want to drop down to 2000 tokens?
```

**Response:** 
```
If you set a 2,000 token limit, the following will happen:

* **Estimated Savings:** 2,500 tokens
* **Action:** 15 middle-tier messages will be removed.
* **Result:** Your core instructions and recent buffer will remain intact.
```

**Prompt:** 
```
Clean up my history so I am under 3000 tokens.
```

**Response:** 
```
The reduction is complete. Your new token total is **2,950**. You successfully saved 1,550 tokens from your previous state.
```

## Capabilities

### Audit token distribution
Check how your tokens are spread across different message tiers.

### Simulate pruning strategies
See exactly what will be removed before you commit to a change.

### Enforce token budgets
Automatically remove low-relevance messages to stay under your limit.

### Protect core instructions
Keep system prompts and initial user messages permanently intact.

### Maintain recent context
Ensure a buffer of the most recent messages remains untouched during cleanup.

## Use Cases

### 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.

### Complex research tasks
Keep your core research goals in view even after hours of deep diving into different topics and datasets.

### Agentic workflow stability
Prevent agents from hallucinating due to context overflow by maintaining a clean, relevant message history.

## Benefits

- Stop losing track of instructions when conversations get long.
- Avoid hitting token limits by pruning low-relevance messages automatically.
- See exactly how much space you will save before any data is removed.
- Keep your core system prompts safe and immutable during every cleanup.
- Monitor your current usage density to spot trends early.

## How It Works

The bottom line is you keep your most important context without hitting window limits.

1. Check your current token density to see where usage is spiking.
2. Set a target token limit for your active session.
3. Run the reduction process to clear out unnecessary history.

## Frequently Asked Questions

**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 tool 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` tool. 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` tool allows you to estimate how many tokens will be saved and what information will remain without modifying your actual message list.