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Make your AI Agent change code with fewer cleanup loops. v{{ .Site.Params.version }}

MCP Steroid gives your AI Agent the same semantic actions a JetBrains IDE gives humans — typed refactors, inspections, debugger, test runs — so the Agent finishes in fewer attempts and you spend less time verifying its output. Ships today as a JetBrains IDE plugin.

Built For Two Audiences

For developers

Connect MCP Steroid to Claude, Codex, Gemini, Cursor, or any MCP-capable AI Agent and let it drive your JetBrains IDE on real code. Free, local, ships today.

Install plugin →

For engineering teams

Running AI Agents across a large codebase? We run Proof-of-Concept engagements tuning skills, prompts, and benchmarks to your repos and workflows. You can also submit a scenario from your own work.

Book a PoC →

Why MCP Steroid

Works with any MCP-capable AI Agent — Claude, Codex, Gemini, Cursor, OpenCode, or any other MCP client. Ships today as a JetBrains IDE plugin.

Claude GPT Gemini Codex CLI Cursor OpenCode Any MCP Client

Fewer wasted iterations

AI Agents finish semantic tasks in fewer attempts when they can use real refactorings, inspections, and the debugger instead of guessing through file edits. Measured on selected DPAIA projects.

Closed verify loop

Your AI Agent runs inspections, the build, and the tests through the same IDE humans use to verify their own work — the Agent catches its mistakes before the human review queue does.

Evidence: fewer wasted iterations, not just speed

DPAIA wall-clock results comparing AI Agents with IDE-native semantic actions (MCP Steroid) vs. file-only workflows. On tasks requiring real semantic understanding the Agent finishes in fewer attempts; read the delta as “rework the Agent avoided”:

Case Task With MCP Without MCP Δ
dpaia_jhipster_sample_app-3 Rename ROLE_ADMIN across JHipster app (9 files) 202s 440s −54%
dpaia_empty_maven_springboot3-1 JWT auth from scratch (5+ new files) 288s 396s −27%
dpaia_feature_service-25 Parent-child JPA & Flyway (10 files) 382s 523s −27%
dpaia_feature_service-125 Multi-layer JPA+service+controller (15 files) 788s 1002s −21%
dpaia_spring_petclinic_rest-14 Simple URL prefix replace (7 files) 188s 181s +4%
dpaia_train_ticket-1 Extend OrderRepository JPQL (4 files) 727s 633s +15%

Tasks requiring semantic understanding — refactorings across many files, multi-layer code generation — show the largest reduction in wasted iterations. Simple text replacements perform similarly with or without IDE access. Suggest your project for the next benchmark run.

What Your AI Agent Can Do

Featured Demos

Create Powerful IntelliJ-Driven Skills

Create custom skills for your AI Agent in minutes. Describe what you want, give it an IntelliJ API example, and let the AI Agent iterate. No plugin development needed.

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 guide was written entirely by AI Agents — a real skill created through experimentation with full IDE access.

Ready to give your AI Agents full IDE access? Download the latest release and get started in minutes. See the Skill Factory blog post for a deep dive on building custom skills.

Proof-of-Concept program: We run Proof-of-Concept engagements for company-specific use cases — custom skills, internal tooling integrations, AI Agent workflows tailored to your codebase. Contact Eugene to discuss.

What’s New

Proof-of-Concept & Support

MCP Steroid is built by Eugene Petrenko, with 21 years of JetBrains ecosystem experience.

We run Proof-of-Concept engagements for company-specific use cases — custom skills, internal tooling integrations, AI Agent workflows tailored to your codebase. Learn more about consulting and Proof-of-Concept options.

Beyond GitHub Sponsors, MCP Steroid also accepts in-kind support: compute tokens for evaluation runs, hardware for the multi-IDE test matrix, and licenses for benchmarking and observability tools. If you or your organisation would like to back the project or the research behind it on those terms, reach out — contact details are linked below.

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