# Stop wasting tokens re-uploading screenshots and specs: My MCP setup > Source: > Published: 2026-09-02 18:47:39+00:00 If you use Cursor or Claude Code heavily, you probably know this workflow: You start a new session, drag and drop a bunch of UI screenshots, architecture diagrams, or heavy project specs into the chat, and tell the AI to look at this. It works, but it causes two massive problems: Token Burn (and Credit Drain): Vision tokens and heavy text files are expensive. You waste your API credits processing those same screenshots and docs every single time you spin up a new chat. Context Clutter: The AI's context window gets clogged. Its logic degrades because it’s carrying all that heavy media and text around in its short term memory. I got tired of burning through my API credits on this daily, so I started looking into the Model Context Protocol (MCP). Why MCP is the answer Instead of dumping static files and images directly into the prompt, MCP allows your AI editor to query a local or remote server only when it needs specific information. Think of it like giving Cursor a direct database connection to your project's assets. It indexes the data once, and the AI retrieves just the pieces it needs to answer your specific coding question. The token savings are ridiculous. How I automated this (Building Dokpod) You can build a local MCP server yourself, but managing the indexing for mixed media (images, video walkthroughs, and text), handling local environments, and keeping connections stable became its own headache. So, I built [Dokpod.io] to automate the entire thing. It acts as an AI knowledge vault. You upload your UI screenshots, video walkthroughs, API docs, and codebase context into Dokpod once. It handles the indexing and gives you a simple MCP connection to plug straight into Cursor or Claude. The result: Zero repetitive uploading for images, videos, or text. Massive reduction in input tokens (saving your credits and limits). The AI actually remembers your UI references and architecture across different coding sessions. I need your technical feedback If you are wrestling with context limits or burning through your credits, I’d love for you to try this workflow out. You can check out the vault at [Dokpod.io](https://dokpod.io). I'm currently working on improving the indexing speed for heavier media and adding more native integrations. If you give it a spin in your setup, please tear it apart and let me know what I should build next or what I can improve. Let me know what you think in the comments!