# Why OneTool: Solving the MCP Scaling Crisis

*LinkedIn article draft - based on technical analysis*

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The AI coding landscape faces three converging crises that OneTool directly addresses.

## The Problem

**Context Rot**: LLM performance degrades as input tokens increase. Chroma's research shows the 10,000th token is handled less reliably than the 100th.

**Token Bloat**: Connecting 5 MCP servers consumes ~12K tokens before conversations begin. 10+ servers? Over 25K tokens.

**Vibe Coding at Scale**: 92% of US developers use AI daily, but 41% more debugging time is reported at scale. The "Day 2" problem - maintaining AI-generated systems - remains unsolved.

## The Solution

OneTool reduces token usage from **46,000 to 2,000 tokens** - a 96% reduction ([source](../brand/claims.md)) - by presenting tools as code APIs instead of individual tool definitions.

**One tool, code execution.** Instead of loading 50 separate tools with their schemas, OneTool exposes a single `run` tool. LLMs write Python to call any function:

```python
__ot brave.search(query="AI trends 2026")
```

## Benchmark Results

| Approach | Tokens | Cost | Accuracy |
|----------|--------|------|----------|
| Multiple MCP | 46K | $0.025 | 100% |
| OneTool | 1.9K | $0.002 | 100% |

**96% fewer tokens. Same accuracy. 24x lower cost.**

## Why This Matters

The industry is shifting from vibe coding to **agentic engineering**. OneTool is built for that future:

- Drop-in tool files (no registration, no config)
- AST-based security validation before execution
- Spec-driven development with OpenSpec integration
- Benchmark harness for rigorous LLM + MCP testing

## Get Started

```bash
uv tool install onetool-mcp
```

Add to Claude Code and start using tools through code.

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*Based on Anthropic's research on code execution with MCP and advanced tool use patterns.*

#AI #MCP #DeveloperTools #AgenticEngineering #LLM
