Debugg AI

Enables code generation agents to automatically create and execute end-to-end tests for newly generated code without manual test configuration. The MCP server integrates with the Debugg AI testing platform to provision remote browser environments, execute test suites against code changes, and return pass/fail results with execution logs. Tests run in isolated, ephemeral browser contexts that are spun up on-demand and torn down after execution, eliminating local environment setup overhead.

Exposes test execution capabilities as MCP tools that can be discovered and invoked by compatible agent frameworks (Claude, Cline, custom LLM agents). The MCP server implements the Model Context Protocol specification to register test execution functions with standardized schemas, allowing agents to call testing functionality through their native tool-calling mechanisms. Tool schemas define input parameters (test code, target code, configuration) and output structure (results, logs, artifacts), enabling agents to understand and reason about test execution before invoking it.

Provisions temporary, isolated browser environments in the Debugg AI cloud infrastructure for each test execution, ensuring test isolation and preventing state leakage between runs. The system creates a fresh browser instance, executes the test code within that context, captures execution artifacts (logs, screenshots, network traces), and tears down the environment after completion. This approach eliminates local browser setup requirements and ensures consistent test execution across different agent execution contexts.

Collects test execution results, logs, and artifacts from remote browser environments and returns them in a structured format that agents can parse and reason about. The system aggregates pass/fail status, execution time, error messages, console logs, and optional artifacts (screenshots, videos) into a unified result object. This structured output enables agents to make decisions about code quality, determine whether to iterate on generated code, or escalate failures for human review.

Enables agents to pass newly generated code or code changes to the test execution environment, ensuring tests run against the exact code the agent generated. The system accepts code as input (either as inline strings or file references), injects it into the remote browser environment, and executes tests against that code. This capability bridges the gap between code generation and test execution, allowing agents to validate their own output without manual file management or deployment steps.