# Context Window Eviction Planner MCP for AI Agents AI Agent Connect

> Context Window Eviction Planner manages LLM context limits by calculating the most efficient way to prune conversation history. It uses deterministic logic to decide which messages to remove based on their importance and age, ensuring your agent stays within token bounds without losing the core meaning of the discussion.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_r3Tjz2PlnpwYtOb3aE3qfzdh9jQjX01R6EJMejND/ai-agent-connect
- **Tags:** context-window, token-management, llm-optimization, eviction-logic, efficiency

## Description

Long-running conversations eventually hit a wall when the context window fills up. When that happens, your AI client starts forgetting the beginning of the chat or, worse, fails entirely. This MCP solves that by acting as a precise management layer for your conversation history. Instead of blindly deleting old messages and losing critical context, you can use this to figure out exactly what to prune to keep the conversation flowing smoothly. It looks at how much weight each message carries and how much space is left, then builds a plan to clear out the noise while keeping the signal. You can monitor how much pressure is building up in your current session and get ahead of the overflow before it happens. By connecting this to your workflow through the Vinkius catalog, you stop fighting with token limits and start focusing on the actual task at hand. It turns a messy, unpredictable technical constraint into a predictable, manageable part of your agent's memory.

## Tools

### get_message_metadata
Summarizes the importance and weight of your current conversation history. It helps you understand the density of your context.

### analyze_window_efficiency
Evaluates how well your current context is being used. It flags when you are running out of room.

### calculate_eviction_plan
Creates a specific list of messages to remove to fit within a target token limit. It keeps the most relevant data intact.

## Prompt Examples

**Prompt:** 
```
I'm hitting my limit. Can you figure out what to remove to get me down to 3000 tokens?
```

**Response:** 
```
I've analyzed your current history. To reach your 3000 token target, I recommend removing the following:

| Message ID | Reason |
| :--- | :--- |
| #14 | Outdated terminal output |
| #22 | Redundant greeting |
| #25 | Intermediate calculation step |

This plan will bring you to approximately **2,850 tokens** while maintaining a relevance score of **0.88**.
```

**Prompt:** 
```
How healthy is my current conversation window?
```

**Response:** 
```
Your context window is currently at **82% capacity**. 

* **Status:** Warning
* **Remaining Space:** ~500 tokens
* **Efficiency:** High

I suggest preparing an eviction plan soon to avoid a sudden overflow.
```

**Prompt:** 
```
Which parts of this chat are actually the most important right now?
```

**Response:** 
```
Based on the current conversation weight, here is the breakdown:

* **Primary Context:** The initial project requirements (Weight: **0.95**)
* **Secondary Context:** The recent debugging steps (Weight: **0.72**)
* **Low Priority:** The conversational filler and greetings (Weight: **0.15**)

I recommend prioritizing the project requirements during any future pruning.
```

## Capabilities

### Calculate precise message removal plans
Determine exactly which parts of a conversation to drop to stay under a specific token limit.

### Monitor context pressure
Check how efficiently your current conversation is using its available memory space.

### Assess conversation importance
Get a breakdown of the weight and relevance of your current message history.

### Prevent context overflow
Identify high-risk scenarios where the conversation is about to hit its technical limit.

### Optimize information retention
Minimize data loss by choosing to remove less relevant messages first.

## Use Cases

### Long-form coding sessions
A developer working on a massive codebase uses calculate_eviction_plan to clear out old terminal outputs while keeping the core architecture instructions in the window.

### Complex research agents
An agent researching a deep topic uses analyze_window_efficiency to monitor when it needs to summarize its findings to make room for new data.

### Multi-turn customer support bots
A support agent uses get_message_metadata to ensure the user's original problem remains the most 'weighted' part of the history during a long troubleshooting session.

### Automated data analysis
A data analyst agent uses calculate_eviction_plan to manage massive logs, ensuring the most recent data points stay available for reasoning.

## Benefits

- Stop losing critical context by using calculate_eviction_plan to prune only the least important messages.
- Avoid sudden conversation crashes by using analyze_window_efficiency to spot high-risk token usage early.
- Maintain high-quality reasoning by using get_message_metadata to track the weight of your conversation history.
- Reduce token costs by keeping your context window lean and focused on relevant data.
- Keep long-running agent sessions stable without manual intervention or constant resets.

## How It Works

The bottom line is you get a mathematical way to keep your AI's memory clean and functional.

1. Connect the MCP to your preferred AI client through Vinkius.
2. Request a status check or a specific removal plan based on your current token usage.
3. Receive a structured plan to prune messages or a summary of your current context efficiency.

## Frequently Asked Questions

**How does the Context Window Eviction Planner keep my AI from forgetting things?**
It calculates which messages are the least important based on their relevance and age. Instead of deleting everything, it surgically removes the 'noise' so the most important parts of your conversation stay in the window.

**Can I use the Context Window Eviction Planner with Claude or Cursor?**
Yes. As long as your client is MCP-compatible, like Claude, Cursor, or Windsurf, you can connect this to manage your context limits directly within those apps.

**Will this MCP help me save money on token usage?**
Yes. By helping you prune unnecessary or redundant messages, it keeps your context window lean, which can lead to more efficient token consumption during long sessions.

**How do I know when I'm about to run out of context space?**
You can check your context pressure at any time. The tool provides a way to monitor how much of your window is being used and flags when you are approaching the limit.

**Is the message removal process random?**
No, it is deterministic. The tool uses specific logic to weigh the importance of messages, ensuring that the removal process is optimized to keep the most relevant information available.