Bringing Agent Evals Into Your IDE: Introducing Galileo's Agent Evals MCP

Apply eval-powered insights where you actually build

The feedback loop for AI development is inefficient. When your agent fails in testing, you're forced to context switch between your IDE, dashboards, logs, and evaluation platforms to understand what went wrong and how to fix it. Each transition costs cognitive overhead and slows down iteration cycles that should be measured in minutes, not hours.

Today, we're launching Galileo's Agent Evals MCP to eliminate this artificial boundary between building and evaluating AI agents.

From Dashboard to Development Environment

Model Context Protocol (MCP) servers transform how developers interact with AI tooling. By bringing Galileo's evaluation and observability capabilities directly into Cursor and VS Code, we're enabling a development workflow that was previously impossible: get root cause analysis, generate synthetic test data, and apply fixes without ever leaving your IDE.

Our MCP server transforms your IDE's AI assistant into an eval-powered copilot. With natural language commands, you can now:

• Generate synthetic test datasets on demand to simulate edge cases and failure scenarios

• Access logstream insights that pinpoint precisely where and why agents deviate from expected behavior

• Set up and validate prompt templates directly in your development environment

• Instrument your codebase with Galileo observability as your AI assistant suggests and applies integration code

• Tab complete your way to fixes by going from improvement insights and root causes, directly to generated solutions

The result? Agent reliability that starts where you code, not where you deploy.

Why This Matters for Agent Development

As agentic AI systems become more complex, the traditional evaluate-after-deployment model creates unacceptable risk and delay. Production issues that could have been caught during development slip through, causing customer-facing failures that erode trust.

Galileo’s Agent Evals MCP addresses this by bringing disciplined experimentation and quality assurance directly into the development process to enable evaluation-driven development. When you can generate test cases, run evaluations, and analyze failures in the same environment where you're writing code, you catch issues earlier and ship more reliable agents faster.

Getting Started

Setting up the Galileo MCP takes just minutes. With one configuration file, you'll have access to our complete evaluation platform from within your IDE of choice. No manual copy-paste, no context switching, just eval-driven insights integrated into your natural workflow.

Ready to transform your agent development process? Explore our setup documentation and start building more reliable agents today.

Galileo helps AI teams ship production-ready agents through comprehensive evaluation, observability, and now IDE-native tooling. Sign up free here.