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Show HN: MCP server gives your agent a budget (save tokens, get smarter results)
As a consultant I foot my own Cursor bills, and last month was $1,263. Opus is too good not to use, but there's no way to cap spending per session. After blowing through my Ultra limit, I realized how token-hungry Cursor + Opus really is. It spins up sub-agents, balloons the context window, and suddenly, a task I expected to cost $2 comes back at $8. My bill kept going up, but was I really going to switch to a worse model?<p>No. So I built l6e: an MCP server that gives your agent the ability to budget. It works with Cursor, Claude Code, Windsurf, Openclaw, and every MCP-compatible application.<p>Saving money was why I built it, but what surprised me was that the process of budgeting changed the agent's behavior. An agent that understands the limitations of the resources doesn't try to speculatively increase the context window with extra files. It doesn't try to reach every possible API. The agent plans ahead, sticks to it, and ends work when it should.<p>It works, and we've been dogfooding it hard. After v1 shipped, the rest of l6e was all built with it. We launched the entire docs site using frontier models for $0.99. The kicker was every time l6e broke in development, I could feel the pain. The agent got sloppy, burned through context, and output quality dropped right along with it.<p>Install: pip install l6e-mcp<p>Docs: <a href="https://docs.l6e.ai" rel="nofollow">https://docs.l6e.ai</a><p>GitHub: <a href="https://github.com/l6e-ai/l6e-mcp" rel="nofollow">https://github.com/l6e-ai/l6e-mcp</a><p>Website: <a href="https://l6e.ai" rel="nofollow">https://l6e.ai</a><p>Happy to answer questions about the system design, calibration models, or why I can't go back to coding without it.
The product addresses a high-growth pain point in the AI agent space—unpredictable token costs—and offers a unique insight into how resource constraints improve agent reasoning. However, it currently functions more as a utility tool than a standalone company, facing significant platform risk from IDEs like Cursor or model providers like Anthropic who could implement native budgeting.