AI tool comparison
CodeBurn vs tldr MCP Gateway
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
CodeBurn
Token cost analytics and waste finder for AI coding tools
75%
Panel ship
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Community
Paid
Entry
CodeBurn is an open-source terminal dashboard that tracks and analyzes your token spend across Claude Code, OpenAI Codex, Cursor, OpenCode, and GitHub Copilot. It classifies coding sessions into 13 activity types — architecture, debugging, refactoring, code review, and more — and shows you exactly where your tokens are going. The standout feature is the optimizer: CodeBurn identifies wasteful patterns in your workflow — like repeatedly re-reading the same files, bloated context files, or MCP servers that are loaded but never used — and suggests concrete changes with estimated savings. It also tracks one-shot success rates per task type, helping you understand where AI is genuinely saving time vs. where you're fighting the tool. A macOS menu bar widget shows live token spend as you work, with a daily budget alert. Built by indie developer AgentSeal and shared as a Show HN, it picked up 80 upvotes and significant interest from developers who didn't realize how much they were spending on context re-reads alone. Open source under MIT license.
Developer Tools
tldr MCP Gateway
Shrink 41+ MCP tool schemas by 86% before they hit your model
75%
Panel ship
—
Community
Paid
Entry
tldr is a local proxy that sits between your AI coding harness and upstream MCP servers, solving one of the most underappreciated problems in agentic workflows: context bloat from tool schema proliferation. When you connect GitHub MCP, filesystem MCP, and a few others, you can easily be sending 24,000+ tokens of tool schemas to the model before any work begins. Instead of passing all those schemas directly, tldr exposes exactly five wrapper tools to the model: search_tools, execute_plan, call_raw, inspect_tool, and get_result. The model learns which underlying tools exist on-demand through search_tools, then calls them through the proxy. GitHub MCP's 24,473-token schema surface compresses to 3,482 tokens — an 86% reduction. Output responses are further compressed through field stripping, a 4,096-token cap, and a 64KB byte limit. This is a genuinely practical solution for power users running multi-MCP setups who've noticed degraded performance as their tool count grows. The tradeoff is one extra hop of indirection, but the token savings pay for themselves in improved model attention and lower API costs.
Reviewer scorecard
“I ran this on a week of Claude Code sessions and immediately found I was spending 30% of my tokens re-reading the same five config files. The menu bar widget is the killer feature — seeing the cost counter tick up while you work changes your behavior instantly. Instant install for anyone serious about AI coding.”
“This solves a real problem I've hit personally — when you connect enough MCP servers, you're wasting a quarter of your context window on tool definitions before a single line of code is written. The five-wrapper-tool approach is elegant and the compression numbers are concrete and reproducible.”
“The 13 activity categories feel arbitrary and require calibration. More importantly, this is fundamentally a symptom-treating tool — the real fix is better context management built into the AI tools themselves. And if you're on a flat-rate API plan, cost tracking is largely irrelevant.”
“This is a workaround for a problem that MCP server authors and model providers should fix natively. Adding another proxy layer to your local development setup increases debugging complexity, and the 4,096-token output cap could silently truncate important data from tool responses.”
“Observability for AI token usage is an entire category about to explode. As agentic workflows scale from individual developers to teams and enterprises, understanding where tokens go becomes as important as understanding where CPU cycles go. CodeBurn is early but directionally correct.”
“Schema proliferation is becoming a real scalability ceiling for agentic systems. tldr's dynamic tool discovery approach — where the model learns which tools exist on-demand — hints at how future agent routing layers will work at scale across hundreds of specialized MCP endpoints.”
“Even for non-coding creative work — writing, research, brainstorming — understanding which prompting patterns are wasteful vs. effective is valuable. The one-shot success rate tracking by task type is a genuinely novel idea I haven't seen anywhere else.”
“For anyone using AI agents to manage creative workflows across multiple platforms, the context savings translate directly to more coherent, focused outputs. Less schema bloat means the model spends more attention on your actual task.”
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