---
title: "Strategy"
description: "How MCP Steroid helps your AI Agent change code with fewer cleanup loops"
weight: 30
group: "Vision"
aliases:
  - /strategy/
---

## Reliable code changes for AI Agents

MCP Steroid makes your AI Agent change code with fewer cleanup loops. File-only workflows break on real tasks because the
Agent cannot run inspections, execute refactorings, launch debugger flows, or use live IDE context and actions. The larger
the repository, the more research the Agent must do -- and the more tokens it burns -- without IDE-grade understanding.

MCP Steroid closes that gap by giving AI Agents the same semantic actions JetBrains IDEs give humans -- typed refactors,
inspections, debugger, test runs -- so the Agent finishes in fewer attempts and humans spend less time verifying its output.

## Strategic thesis

MCP Steroid is an AI-Agent-first product. Today the value is delivered as a JetBrains IDE plugin, which is where existing
users already work; the long-term product direction is the same surface in a headless runtime so it serves AI Agents anywhere
they execute, not only when a developer's IDE is open.

On tasks that depend on IDE capabilities, AI Agents with MCP Steroid should complete more work with fewer interventions,
lower token usage, and less rework on the human side than the same AI Agents without it.

## Three-phase product arc

1. **Phase 1 -- Plugin distribution:** ship the surface as a JetBrains IDE plugin (current)
2. **Phase 2 -- Fine-tune:** evals, benchmarks, prompt optimization
3. **Phase 3 -- Scale:** headless mode, packaging, SaaS, B2B distribution

### Phase 1: Plugin distribution (current)

Today MCP Steroid runs as a plugin inside JetBrains IDEs. A developer connects their AI Agent 
(Claude Code, Codex, Gemini, or any MCP client) to a running IDE instance -- IntelliJ IDEA, PyCharm, Android Studio, Rider, and others -- where their project is already open.

### Phase 2: Fine-tune -- evals, benchmarks, learn, and iterate

**Result: 20-54% speedup on benchmarks** when AI Agents use MCP Steroid with full IDE access vs. file-only workflows.

DPAIA benchmark results across diverse Spring Boot tasks:

| Task | With MCP | Without MCP | Delta |
|------|----------|-------------|-------|
| Rename ROLE\_ADMIN across JHipster app (9 files) | 202s | 440s | **-54%** |
| JWT auth from scratch (5+ new files) | 288s | 396s | **-27%** |
| Parent-child JPA & Flyway (10 files) | 382s | 523s | **-27%** |
| Multi-layer JPA+service+controller (15 files) | 788s | 1002s | **-21%** |
| Simple URL prefix replace (7 files) | 188s | 181s | +4% |
| Extend OrderRepository JPQL (4 files) | 727s | 633s | +15% |

Tasks requiring semantic understanding -- refactorings across many files, multi-layer code generation -- show the largest gains. Simple text replacements perform similarly with or without IDE access.

We are collecting scenarios and execution logs from real MCP Steroid sessions (share your `.idea/mcp-steroid` folder with us).

The collected data is analyzed to identify sharp edges in the current implementation and to improve prompts, skills,
and documentation. AI Agents help us craft the better product for AI Agents. This is an iterative process; we have
completed roughly seven optimization rounds so far, primarily on the MCP Steroid project itself.

This validation loop is described in [Learning Methodology](/docs/learning-methodology/). See also [IntelliJ as a Skill Factory](/docs/skill-factory/) for how skills turn one-off API explorations into reusable AI Agent capabilities.

### Phase 3: Scale -- headless runtime, SaaS, B2B

The long-term target is a self-contained runtime, available both as SaaS and as an end-user product, that serves as the headless IDE for AI Agents.

## Easy experimentation

MCP Steroid provides an easy way to experiment with new tasks, prompts, and skills locally. Create a new skill, ask your AI Agent to use `steroid_execute_code`, and give it an example code snippet using IntelliJ API to solve your goal:

```kotlin
import com.intellij.psi.search.PsiSearchHelper
import com.intellij.psi.search.GlobalSearchScope

// Example: find all TODO comments in the project
val todoItems = readAction {
    val searchHelper = PsiSearchHelper.getInstance(project)
    val result = mutableListOf<String>()
    searchHelper.processCommentsContainingIdentifier("TODO", GlobalSearchScope.projectScope(project)) { comment ->
        result.add("${comment.containingFile.virtualFile.path}: ${comment.text.trim()}")
        true
    }
    result
}
todoItems.forEach { println(it) }
```

The [Debugging IDE with MCP Steroid](/docs/how-to-debug-ide/) guide was written entirely by AI Agents using this approach -- a real skill created through experimentation with full IDE access.

## How you can help

- **Developers:** submit reproducible scenarios via [Need Your Experiments and Support](/docs/need-your-experiments-and-support/) and engage in the community
- **Engineering leaders:** request pilot evaluations on your repositories -- we are eager to learn alongside you
- **Sponsors and investors:** support benchmark expansion and productization

## Creator

MCP Steroid is built by [Eugene Petrenko](https://linkedin.com/in/jonnyzzz), with 21 years of JetBrains ecosystem experience.

## Contact

- [LinkedIn](https://linkedin.com/in/jonnyzzz)
- [GitHub](https://github.com/jonnyzzz/mcp-steroid)
- [GitHub Sponsors](https://github.com/sponsors/jonnyzzz)
