AI Context Kit MCP server

ai-context-kit

How do you measure the token cost of your context?

You spent hours writing the perfect .md context file, just to find out that your agent got worse. That's not a bug. That's what happens when nobody measures the cost of context.

     

 

You write a CLAUDE.md. Then someone adds .cursor/rules/. Then a teammate drops in an AGENTS.md. Then someone copies in a .cursorrules file from a blog post. Nobody removes the old ones.

Six months later your project has four context files that overlap, contradict each other, and dump 8,000 tokens of directory listings and "follow best practices" into every conversation. Your agent follows all of it. It gets slower. It gets confused. You blame the model.

An ETH Zurich study (February 2026) measured what actually happens when you give agents context files:

• Auto-generated context files reduced task success compared to providing nothing
• Human-written ones only improved accuracy by 4%
• Inference costs jumped 20%+ from wasted tokens
• Performance dropped on some models because agents got too obedient - following unnecessary instructions instead of solving the actual problem

I kept hitting this in my own projects, so I built ai-context-kit - a toolkit to treat context like a budget. Measure it, trim it, inject only what the current task needs.

import { loadRules, measure, lint, select } from "ai-context-kit";

const rules = await loadRules("./");

measure(rules, 4000); // what does your context cost?
lint(rules); // conflicts? duplicates? dead weight?
select(rules, {
  task: "fix auth bug", // only inject what matters
  budget: 2000, // stay within token budget
});

Quick Start

npm install ai-context-kit

Run the CLI on any project to see what you're actually injecting:

npx ai-context-kit measure

Shortened. The full README is on GitHub.