# Context Window Optimizer MCP for AI Agents AI Agent Connect

> Context Window Optimizer manages the limited memory of your AI agents. It uses a greedy selection algorithm to pick the most relevant and recent information, ensuring your agent stays within token limits without losing the critical details needed to complete complex tasks.

## Overview
- **Category:** optimization
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_YT9BcHeK3qV8GHjTDUXR83vcT9oI2MLYejbyO1oc/ai-agent-connect
- **Tags:** token-optimization, context-window, llm, greedy-selection, relevance-scoring

## Description

When you're running complex workflows, your AI agent eventually hits a wall. The context window fills up with noise, old data, or irrelevant chatter, causing the agent to lose its train of thought or start hallucinating. This MCP fixes that by acting as a smart filter for your agent's memory. Instead of dumping every single piece of data into the prompt, it intelligently selects only the most important bits based on how relevant they are to the current task and how recently they occurred. This keeps your token usage low and your agent's focus sharp. By connecting this to your preferred AI client through the Vinkius catalog, you stop fighting with token limits and start building agents that actually remember what matters. It's about making sure the most important information is always front and center when the agent needs to make a decision.

## Tools

### evaluate_selection_efficiency
Measures how effectively the context was utilized relative to the total available budget

### filter_by_relevance_threshold
Prunes the candidate list to remove low-quality information before performing the optimization

### select_optimal_context
Picks the best pieces of information to fit your token limit. It maximizes relevance so your agent doesn't miss the point.

## Prompt Examples

**Prompt:** 
```
I have these 20 logs from the last hour. Pick the most relevant ones for a 500 token limit.
```

**Response:** 
```
I've analyzed the logs. To stay under your 500 token limit, I've selected the following:

| Log ID | Relevance Score | Tokens |
| :--- | :--- | :--- |
| `log_882` | 0.95 | 120 |
| `log_885` | 0.88 | 210 |
| `log_890` | 0.82 | 150 |

**Total Tokens:** 480
```

**Prompt:** 
```
Clean up this conversation history. Remove anything older than 10 minutes.
```

**Response:** 
```
Done. I've filtered the conversation history. 

* **Removed:** 5 outdated messages.
* **Retained:** 12 recent messages.

Your agent is now working with a fresh context window.
```

**Prompt:** 
```
How efficient was the context I just sent you?
```

**Response:** 
```
The context selection is highly efficient:

* **Utilization Ratio:** 0.94
* **Density Score:** 0.89

Most of the tokens provided are directly relevant to the current task, meaning very little space is being wasted on noise.
```

## Capabilities

### Trim token usage
Keep your agent within its memory limits by discarding useless data.

### Prioritize fresh data
Ensure your agent focuses on the most recent information available.

### Maximize information density
Pack the most relevant facts into every prompt sent to your agent.

### Audit context quality
Check how well your selected information actually serves the task.

### Clean up old context
Automatically remove outdated information that no longer applies.

## Use Cases

### Long-running research tasks
An agent researching a topic for hours accumulates massive logs. Use this to keep only the key findings in the active window.

### Multi-turn customer support
A support agent needs to remember the user's problem without re-reading a 50-message transcript every time.

### Complex coding workflows
When an agent is working across dozens of files, use this to feed it only the relevant code snippets instead of the whole repo.

### Autonomous data analysis
An agent processing a massive CSV needs to focus on specific rows without overflowing its memory.

## Benefits

- Stop hitting token limits by using select_optimal_context to prune unnecessary data.
- Improve agent accuracy by using filter_by_recency to keep the focus on current events.
- Lower your API costs by reducing the number of tokens sent in every request.
- Get better reasoning by using evaluate_context_efficiency to ensure high information density.
- Prevent hallucinations by removing outdated or conflicting information from the context.

## How It Works

The bottom line is that your agent gets exactly the information it needs to act, without the bloat.

1. Connect your AI client to the MCP via Vinkius.
2. Provide a pool of candidate information or previous conversation history.
3. The agent receives a condensed, high-relevance prompt that fits perfectly within its token budget.

## Frequently Asked Questions

**How does Context Window Optimizer help with token costs?**
It reduces the number of tokens you send to your AI client by filtering out irrelevant data. Fewer tokens mean lower costs for every request your agent makes.

**Can I use Context Window Optimizer with Claude or Cursor?**
Yes. Since it follows the standard protocol, you can connect it to any compatible client like Claude, Cursor, or Windsurf through Vinkius.

**Will this make my AI agent smarter?**
It makes your agent more focused. By removing noise and keeping only relevant, recent information, the agent is less likely to get confused or hallucinate.

**Does Context Window Optimizer replace RAG?**
Not exactly. RAG finds the data, while this MCP optimizes the specific pieces of data you've already found to ensure they fit perfectly into the prompt.

**How does it decide what information is important?**
It uses a mathematical approach that looks at how relevant a piece of information is to your goal and how recently it happened, ensuring the most useful data stays in the window.

**How does the selection process work?**
The process uses `select_optimal_context` to sort items by relevance score and recency, then greedily adds them until the token limit is reached.

**Can I reserve specific messages?**
Yes, you can use the reservation rules in `select_optimal_context` to ensure the system prompt and the last user message are always included.

**How do I measure if my context is efficient?**
You can use the `evaluate_context_efficiency` tool to get a report on utilization ratio, density score, and coverage gap.

**What is the optimization score?**
The optimization score is a metric returned by the selection tool that represents the aggregate relevance of the items successfully selected within your budget.