# Agent Context Window Manager MCP for AI Agents AI Agent Connect > Agent Context Window Manager tracks token usage and manages conversation health for long-running AI sessions. It prevents context overflow by monitoring remaining space, planning compression strategies, and analyzing how tool calls fragment your conversation history to keep your agent sharp and focused. ## Overview - **Category:** utilities - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_BPghAk5oXGL3BsDHMefc5S6Sn9VO570vQFs9Cbiv/ai-agent-connect - **Tags:** tokens, context-window, compression, optimization, llm-health ## Description Long conversations with your AI client eventually hit a wall. As the chat grows, the agent starts losing the thread, forgetting early instructions, or becoming sluggish because the context window is stuffed with redundant data. This MCP keeps your agent's memory clean and efficient. Instead of guessing how much room is left, you can get exact numbers on your remaining token capacity. When a conversation gets too heavy, you can decide exactly how to shrink the history without losing the important bits. It also helps you see if your agent is spending too much space on tool outputs versus actual dialogue, which is usually what causes a sudden drop in intelligence. By connecting this to your workflow through the Vinkius catalog, you stop fighting against context limits and start managing them. You'll spend less time restarting chats and more time working with an agent that actually remembers the project constraints. ## Tools ### analyze_fragmentation Checks if tool outputs are crowding out the actual conversation. It helps you see if your agent is becoming too tool-heavy. ### calculate_remaining_context Tells you exactly how many tokens are left in the current window. Use this to avoid sudden context overflows. ### plan_compression Creates a strategy to shrink your chat history. It helps you decide what to keep and what to summarize. ## Prompt Examples **Prompt:** ``` How much space is left in my current conversation? ``` **Response:** ``` You have **45,200 tokens** remaining before you hit the limit. You are currently at about 60% capacity. ``` **Prompt:** ``` I'm running low on space. How should I clean up this chat? ``` **Response:** ``` I recommend a **sliding window compression**: * **Keep:** The last 5 turns of dialogue in full. * **Summarize:** All previous turns into a concise 5:1 ratio summary. * **Discard:** Large JSON outputs from previous tool calls. ``` **Prompt:** ``` Is the conversation getting too cluttered with tool data? ``` **Response:** ``` Yes. Your fragmentation ratio is **0.65**, which indicates a **Tool Heavy** status. The tool outputs are currently occupying a significant portion of your context compared to the actual dialogue. ``` ## Capabilities ### Monitor token headroom Get a real-time count of exactly how many tokens remain before your agent hits its limit. ### Prepare for context overflows Generate specific plans to compress your conversation history before the window fills up. ### Check conversation health Identify if tool-heavy interactions are cluttering your context and making the agent less effective. ### Optimize memory usage Keep the most relevant parts of a long chat while stripping away the noise. ## Use Cases ### Managing long coding sessions When a coding agent has read dozens of files, use plan_compression to summarize the early architecture discussions so it doesn't forget the core requirements. ### Debugging agentic loops If an agent is stuck in a loop, use analyze_fragmentation to see if massive tool outputs are drowning out the original goal. ### Cost-conscious development Use calculate_remaining_context to track how close you are to the limit, preventing expensive re-runs of massive context windows. ### Maintaining persona consistency Ensure a character-driven agent doesn't lose its personality by managing how much dialogue is preserved versus summarized. ## Benefits - Stop losing track of instructions by using calculate_remaining_context to monitor your limits. - Prevent sudden hallucinations by using plan_compression to tidy up messy chat histories. - Keep your agent's reasoning sharp by using analyze_fragmentation to spot tool-heavy clutter. - Reduce latency and costs by keeping the context window lean and relevant. - Maintain continuity in long-running tasks without needing to restart the chat constantly. ## How It Works The bottom line is you get a precise way to prevent your AI from getting 'stupid' due to context bloat. 1. Connect the MCP to your preferred AI client via Vinkius 2. Ask your agent to check its current token status or fragmentation levels 3. Apply a compression plan to clear space when the window gets crowded ## Frequently Asked Questions **How can Agent Context Window Manager prevent my AI from forgetting things?** It allows you to monitor your token usage and proactively compress your conversation history. By summarizing old parts of the chat, you keep the most important information within the active window. **Can I use Agent Context Window Manager with Claude or Cursor?** Yes, as long as your client is MCP-compatible, you can connect this to Claude, Cursor, Windsurf, and VS Code to manage your conversation health. **How does Agent Context Window Manager help with token costs?** By helping you plan when to compress or prune your history, it prevents you from sending massive, redundant blocks of text to your AI client, which keeps your token usage efficient. **What does it mean if my conversation is 'Tool Heavy'?** This means the data returned by your tools is taking up most of your context window, leaving very little room for the actual conversation. You can use this to decide when to clear out old tool results. **Does Agent Context Window Manager work for long coding sessions?** Absolutely. It is specifically designed for long-running tasks where the history of file reads and code edits can quickly overwhelm the AI's memory. **How does the system calculate remaining tokens?** The `calculate_remaining_context` tool subtracts system prompts, tool definitions, conversation history, and multimodal tiles from the total window size. **What is context fragmentation?** Fragmentation is the ratio of tokens used by tool calls and outputs compared to the actual conversational text. You can monitor this with `analyze_fragmentation`. **When should I trigger a compression event?** A compression event is recommended when usage reaches the Yellow Tier (70-80%) and is mandatory at the Orange Tier (80-95%).