{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-debugg-ai","slug":"debugg-ai","name":"Debugg AI","type":"mcp","url":"https://github.com/debugg-ai/debugg-ai-mcp","page_url":"https://unfragile.ai/debugg-ai","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-debugg-ai__cap_0","uri":"capability://automation.workflow.0.config.end.to.end.test.generation.and.execution.against.code.changes","name":"0-config end-to-end test generation and execution against code changes","description":"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.","intents":["I want my code generation agent to automatically validate that generated code works end-to-end before committing it","I need to run browser-based tests against code changes without setting up local test infrastructure","I want to catch regressions in generated code by running full E2E test suites in remote browsers","I need test execution feedback integrated into my agent's decision loop to decide whether to iterate on generated code"],"best_for":["AI code generation agents (Devin, Claude with tool use, custom LLM agents) that need validation loops","teams building autonomous code generation systems with quality gates","developers integrating testing into multi-step agent workflows"],"limitations":["Requires active Debugg AI platform account and API credentials — cannot run tests without remote infrastructure access","Test execution latency depends on remote browser provisioning time (typically 5-15 seconds per test run)","Limited to browser-based testing scenarios — cannot test backend-only or CLI-only code without additional setup","No built-in test generation — agents must write test code themselves or use separate test generation tools","Test results are asynchronous — agents must implement polling or callback handling to wait for test completion"],"requires":["Debugg AI platform account with valid API credentials","MCP-compatible agent framework (Claude Desktop, Cline, or custom MCP client)","Node.js 16+ or Python 3.8+ runtime for MCP server","Network access to Debugg AI remote testing infrastructure"],"input_types":["test code (JavaScript/TypeScript, Python, or other browser-compatible languages)","code changes or new code to test (as strings or file paths)","test configuration metadata (test name, timeout, browser type)"],"output_types":["structured test results (pass/fail status, execution time, error messages)","browser console logs and network logs","screenshot or video artifacts from test execution"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-debugg-ai__cap_1","uri":"capability://tool.use.integration.mcp.based.test.execution.tool.registration.for.agent.frameworks","name":"mcp-based test execution tool registration for agent frameworks","description":"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.","intents":["I want my agent framework to discover and call test execution as a native tool without custom integration code","I need standardized tool schemas so my agent can reason about test execution parameters and results","I want to integrate testing into my agent's multi-step workflows alongside code generation and other tools"],"best_for":["MCP-compatible agent frameworks (Claude Desktop, Cline, custom MCP clients)","developers building multi-tool agent workflows that need testing as a first-class capability","teams standardizing on MCP for agent tool integration"],"limitations":["MCP protocol overhead adds ~50-100ms latency per tool invocation compared to direct library calls","Requires agent framework to support MCP — not compatible with older or non-MCP-aware frameworks","Tool schema complexity may limit agent's ability to reason about advanced test configurations","No built-in retry logic — agents must implement their own retry strategies for flaky tests"],"requires":["MCP-compatible agent framework with tool-calling support","Debugg AI MCP server running and accessible to agent","Agent framework configured with MCP server endpoint"],"input_types":["MCP tool invocation with structured parameters (test code, configuration)"],"output_types":["MCP tool result with structured test execution response (status, logs, artifacts)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-debugg-ai__cap_2","uri":"capability://automation.workflow.remote.browser.test.execution.with.isolated.ephemeral.environments","name":"remote browser test execution with isolated ephemeral environments","description":"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.","intents":["I want to run browser tests without installing or managing local browser binaries","I need isolated test environments so tests don't interfere with each other or with my local development","I want consistent test execution regardless of the agent's host environment or OS"],"best_for":["agents running in cloud or containerized environments without local browser access","teams needing test isolation and reproducibility across multiple agent instances","developers testing code that requires specific browser versions or configurations"],"limitations":["Remote execution latency (5-15 seconds per test) is slower than local browser testing","Limited to browser-based testing — cannot test Node.js backend code or CLI tools directly","Browser version and configuration options are limited to what Debugg AI platform provides","Network latency may affect test timing and reliability for time-sensitive tests"],"requires":["Debugg AI platform account with remote browser provisioning enabled","Network connectivity to Debugg AI infrastructure","Test code compatible with remote browser execution (no local file system access)"],"input_types":["test code (JavaScript/TypeScript)","target code or URL to test","browser configuration (type, version, viewport size)"],"output_types":["test execution status (pass/fail/error)","browser console logs and network logs","screenshots or video recordings of test execution","performance metrics (execution time, resource usage)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-debugg-ai__cap_3","uri":"capability://data.processing.analysis.test.result.aggregation.and.structured.reporting.for.agent.decision.making","name":"test result aggregation and structured reporting for agent decision-making","description":"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.","intents":["I want my agent to understand test results and decide whether generated code is acceptable or needs iteration","I need detailed error information from failed tests so my agent can understand what went wrong","I want to track test execution metrics (time, resource usage) to optimize agent-generated code"],"best_for":["agents implementing quality gates or validation loops for generated code","teams building autonomous code generation systems with feedback mechanisms","developers needing detailed test failure diagnostics for debugging agent-generated code"],"limitations":["Result structure is fixed by Debugg AI platform — agents cannot customize result format","Large test artifacts (videos, detailed logs) may exceed agent context window limits","No built-in result filtering or summarization — agents must implement their own logic to extract relevant information","Historical result storage is limited — agents must implement their own persistence if needed"],"requires":["Debugg AI platform returning structured test results","Agent framework capable of parsing and reasoning about structured JSON results"],"input_types":["raw test execution results from remote browser environment"],"output_types":["structured test result object with status, metrics, logs, and artifacts","agent-readable format (JSON) for decision-making"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-debugg-ai__cap_4","uri":"capability://automation.workflow.code.change.context.passing.from.agent.to.test.execution","name":"code change context passing from agent to test execution","description":"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.","intents":["I want to test code my agent just generated without manually copying it to a test environment","I need to pass code changes from my agent's generation step directly to the test execution step","I want to test multiple code variations by passing different generated code to the same test suite"],"best_for":["agents implementing iterative code generation with validation loops","multi-step agent workflows where code generation feeds directly into testing","teams building autonomous code refinement systems"],"limitations":["Code injection mechanism may have limitations on code size or complexity","No built-in code validation — agents must ensure generated code is syntactically correct before passing to tests","Limited to code that can run in browser environment — backend code requires additional setup","No version control integration — agents must manage code history themselves"],"requires":["Generated code in string format or accessible file path","Code compatible with remote browser execution environment"],"input_types":["code as string (JavaScript/TypeScript)","file path to generated code","code metadata (language, entry point, dependencies)"],"output_types":["confirmation that code was injected into test environment","test results against injected code"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Debugg AI platform account with valid API credentials","MCP-compatible agent framework (Claude Desktop, Cline, or custom MCP client)","Node.js 16+ or Python 3.8+ runtime for MCP server","Network access to Debugg AI remote testing infrastructure","MCP-compatible agent framework with tool-calling support","Debugg AI MCP server running and accessible to agent","Agent framework configured with MCP server endpoint","Debugg AI platform account with remote browser provisioning enabled","Network connectivity to Debugg AI infrastructure","Test code compatible with remote browser execution (no local file system access)"],"failure_modes":["Requires active Debugg AI platform account and API credentials — cannot run tests without remote infrastructure access","Test execution latency depends on remote browser provisioning time (typically 5-15 seconds per test run)","Limited to browser-based testing scenarios — cannot test backend-only or CLI-only code without additional setup","No built-in test generation — agents must write test code themselves or use separate test generation tools","Test results are asynchronous — agents must implement polling or callback handling to wait for test completion","MCP protocol overhead adds ~50-100ms latency per tool invocation compared to direct library calls","Requires agent framework to support MCP — not compatible with older or non-MCP-aware frameworks","Tool schema complexity may limit agent's ability to reason about advanced test configurations","No built-in retry logic — agents must implement their own retry strategies for flaky tests","Remote execution latency (5-15 seconds per test) is slower than local browser testing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.037Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=debugg-ai","compare_url":"https://unfragile.ai/compare?artifact=debugg-ai"}},"signature":"MhSyGUIhbqIrYYBvT4I7/CKBRAeK+KzyxnFLsYFBCZbLOn+786/RisitWnTpzetmyPmGyi8WzzohrKzSZEa2Ag==","signedAt":"2026-06-22T18:14:46.757Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/debugg-ai","artifact":"https://unfragile.ai/debugg-ai","verify":"https://unfragile.ai/api/v1/verify?slug=debugg-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}