{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_antarikshc-perfetto-mcp","slug":"antarikshc-perfetto-mcp","name":"perfetto-mcp","type":"mcp","url":"https://github.com/antarikshc/perfetto-mcp","page_url":"https://unfragile.ai/antarikshc-perfetto-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:antarikshc/perfetto-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_antarikshc-perfetto-mcp__cap_0","uri":"capability://data.processing.analysis.perfetto.trace.file.parsing.and.structured.extraction","name":"perfetto trace file parsing and structured extraction","description":"Parses binary Perfetto trace files (protobuf format) and extracts structured performance data including CPU scheduling, memory allocation, GPU rendering, and custom events. Implements protobuf deserialization to convert raw trace bytes into queryable event streams with timestamp, process, thread, and category metadata. Exposes trace contents through MCP tools that clients can invoke to inspect performance characteristics without requiring direct Perfetto UI access.","intents":["Extract performance metrics from Perfetto traces programmatically in an LLM context","Query specific events or time ranges from a trace file without opening the Perfetto UI","Integrate trace analysis into automated performance debugging workflows","Feed trace data to LLMs for performance anomaly detection and root cause analysis"],"best_for":["Performance engineers automating trace analysis pipelines","LLM agents performing automated performance debugging","CI/CD systems that need to analyze traces as part of regression testing"],"limitations":["Limited to Perfetto binary format; does not support other trace formats (Chrome DevTools, Linux perf, etc.)","Protobuf schema must match the Perfetto version that generated the trace — schema mismatches cause parsing failures","No built-in trace filtering or aggregation — raw event extraction only; higher-level analysis requires post-processing","Memory usage scales linearly with trace file size; very large traces (>1GB) may cause memory pressure"],"requires":["Perfetto trace file (.pftrace or .pb format)","MCP client implementation (Claude, custom LLM agent, etc.)","Protobuf runtime library compatible with Perfetto schema"],"input_types":["binary Perfetto trace file","file path or file descriptor"],"output_types":["structured JSON event data","trace metadata (duration, process list, thread list)","filtered event subsets"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antarikshc-perfetto-mcp__cap_1","uri":"capability://tool.use.integration.mcp.tool.registration.for.trace.querying","name":"mcp tool registration for trace querying","description":"Implements MCP server protocol to register and expose trace analysis operations as callable tools. Defines tool schemas (name, description, input parameters) that conform to MCP's function-calling specification, allowing LLM clients to discover and invoke trace queries through standard MCP mechanisms. Handles tool invocation routing, parameter validation, and response serialization back to the client.","intents":["Enable LLM clients to discover available trace analysis operations via MCP tool listing","Call trace analysis functions from Claude or other LLM agents with type-safe parameter passing","Integrate trace querying into multi-step LLM reasoning chains without custom API wrappers"],"best_for":["LLM agents that need to analyze performance traces as part of debugging workflows","Teams building MCP-native performance analysis tools","Developers integrating Perfetto analysis into Claude projects"],"limitations":["Tool schemas must be manually defined and kept in sync with underlying trace parsing logic","Parameter validation is schema-based only; complex conditional logic requires custom validation code","No built-in caching of tool results — repeated queries re-parse the trace file"],"requires":["MCP client implementation (Claude, custom agent, etc.)","MCP server runtime (Node.js, Python, or language-specific MCP SDK)","Perfetto trace file accessible to the MCP server process"],"input_types":["tool invocation with JSON parameters","trace file path or identifier"],"output_types":["JSON-serialized tool results","structured trace data or metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antarikshc-perfetto-mcp__cap_2","uri":"capability://data.processing.analysis.trace.event.filtering.and.time.range.queries","name":"trace event filtering and time-range queries","description":"Filters parsed trace events by criteria such as process ID, thread ID, event category, or time window. Implements in-memory filtering logic that scans the event stream and returns matching subsets. Supports range queries (e.g., 'events between timestamp T1 and T2') to isolate performance anomalies or specific execution phases without re-parsing the entire trace.","intents":["Isolate events from a specific process or thread to analyze its performance in isolation","Query events within a time window to correlate performance issues with specific execution phases","Filter by event category (CPU, memory, GPU) to focus analysis on relevant subsystems"],"best_for":["Performance engineers narrowing down root causes in large traces","LLM agents performing targeted performance analysis on specific processes or time ranges","Automated regression testing that needs to extract relevant events from noisy traces"],"limitations":["Filtering is done in-memory after full trace parsing — no index-based acceleration for large traces","Complex multi-criteria filters require sequential filtering passes, adding latency","No support for regex or pattern matching on event names or custom attributes"],"requires":["Parsed trace data in memory","Filter parameters (process ID, thread ID, category, time range, etc.)"],"input_types":["filter criteria (JSON object with process_id, thread_id, category, start_time, end_time)","trace event stream"],"output_types":["filtered event list (JSON array)","event count or summary statistics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antarikshc-perfetto-mcp__cap_3","uri":"capability://data.processing.analysis.trace.metadata.extraction.and.summary.generation","name":"trace metadata extraction and summary generation","description":"Extracts high-level metadata from parsed traces including process list, thread list, trace duration, event counts by category, and timestamp ranges. Generates summary statistics (e.g., 'trace contains 500K CPU events across 8 processes') to give LLMs a quick overview of trace contents without requiring full event enumeration. Implements aggregation logic that scans the event stream once to compute counts and ranges.","intents":["Get a quick summary of trace contents to understand scope and scale before detailed analysis","Identify all processes and threads in a trace to guide further filtering","Determine trace duration and event density to assess trace quality and completeness"],"best_for":["LLM agents that need to understand trace structure before performing detailed analysis","Performance dashboards that display trace summaries to users","Automated systems that validate trace quality before processing"],"limitations":["Summary statistics are computed on-demand and not cached — repeated calls re-scan the event stream","Aggregation is single-pass only; complex statistics (percentiles, distributions) require separate analysis","No support for hierarchical summaries (e.g., events per process per thread)"],"requires":["Parsed trace data in memory"],"input_types":["trace event stream"],"output_types":["JSON object with metadata (process_list, thread_list, duration, event_counts, timestamp_range)","human-readable summary string"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antarikshc-perfetto-mcp__cap_4","uri":"capability://data.processing.analysis.performance.anomaly.detection.via.trace.analysis","name":"performance anomaly detection via trace analysis","description":"Analyzes trace events to identify potential performance issues such as excessive context switches, memory spikes, long blocking operations, or GPU stalls. Implements heuristic-based detection (e.g., 'flag CPU events with >100 context switches per second' or 'alert on memory allocations >100MB in <1s'). Exposes detected anomalies as structured results that LLMs can reason about and correlate with application behavior.","intents":["Automatically identify performance bottlenecks in a trace without manual inspection","Feed detected anomalies to LLM agents for root cause analysis and remediation suggestions","Highlight suspicious patterns (e.g., lock contention, memory leaks) for further investigation"],"best_for":["Automated performance regression testing that needs to flag anomalies","LLM agents performing autonomous performance debugging","Performance engineers triaging large traces to prioritize investigation areas"],"limitations":["Anomaly detection uses fixed heuristics; thresholds may not be appropriate for all workloads or hardware","No machine learning or statistical baselines — cannot detect subtle anomalies relative to normal behavior","False positives are common if thresholds are not tuned for the specific application and hardware","Detection logic is hardcoded; adding new anomaly types requires code changes"],"requires":["Parsed trace data with sufficient event density","Configurable thresholds for anomaly detection (context switch rate, memory spike size, etc.)"],"input_types":["trace event stream","anomaly detection configuration (JSON with thresholds)"],"output_types":["list of detected anomalies (JSON array with type, timestamp, severity, details)","anomaly summary (count by type)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antarikshc-perfetto-mcp__cap_5","uri":"capability://data.processing.analysis.gpu.rendering.and.frame.timing.analysis","name":"gpu rendering and frame timing analysis","description":"Extracts GPU rendering events from Perfetto traces including frame composition, GPU command submission, and rendering latency. Computes frame timing metrics (frame duration, GPU time, CPU-GPU sync points) and identifies frames exceeding target frame rates (e.g., 60fps, 120fps). Provides per-frame breakdown of GPU work and identifies rendering bottlenecks (GPU stalls, CPU-GPU synchronization delays).","intents":["I need to identify frames that missed the 60fps target and why (GPU stall vs CPU-GPU sync)","I want to analyze GPU utilization and find rendering bottlenecks","I need to correlate frame drops with specific GPU operations or CPU events"],"best_for":["Game developers optimizing rendering performance","Android UI performance engineers debugging jank","LLM agents analyzing frame timing issues in graphics-heavy applications"],"limitations":["GPU event availability varies by device and GPU driver — not all GPU operations are traced","Frame timing analysis requires synchronization between CPU and GPU timelines — clock skew can affect accuracy","GPU-specific metrics (shader compilation time, texture upload) may not be available on all platforms"],"requires":["Perfetto trace with GPU rendering events enabled","Frame composition events with start/end timestamps","GPU command buffer submission and completion events"],"input_types":["GPU rendering events","frame composition events","CPU-GPU synchronization points","optional: target frame rate (default 60fps)"],"output_types":["per-frame timing breakdown (CPU time, GPU time, sync wait)","frame rate statistics (FPS, frame time percentiles)","GPU utilization percentages","rendering bottleneck identification"],"categories":["data-processing-analysis","graphics-profiling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antarikshc-perfetto-mcp__cap_6","uri":"capability://data.processing.analysis.trace.comparison.and.regression.detection","name":"trace comparison and regression detection","description":"Compares metrics from multiple Perfetto traces to identify performance regressions or improvements. Computes delta metrics (CPU time difference, memory usage change, frame rate variance) between baseline and test traces and flags statistically significant changes. Supports filtering comparisons by event type, process, or time range. Generates regression reports with affected components and severity scores.","intents":["I need to detect performance regressions between app versions by comparing trace metrics","I want to identify which components regressed (CPU, memory, GPU) in a new build","I need to generate automated regression reports for CI/CD pipelines"],"best_for":["Performance teams running automated regression testing in CI/CD","LLM agents performing comparative performance analysis","Teams tracking performance trends across app releases"],"limitations":["Comparison requires traces with similar workloads — different test scenarios produce unreliable deltas","Statistical significance testing requires multiple traces per condition — single-trace comparison is unreliable","No built-in normalization for device differences — traces from different devices may not be directly comparable"],"requires":["Multiple Perfetto traces (baseline and test)","Parsed metrics from both traces (CPU time, memory, frame rate, etc.)","Optional: statistical significance thresholds and baseline metrics"],"input_types":["baseline trace metrics","test trace metrics","comparison filters (event type, process, time range)","optional: significance thresholds"],"output_types":["delta metrics (absolute and percentage change)","regression severity scores","affected components list","statistical significance indicators"],"categories":["data-processing-analysis","regression-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_antarikshc-perfetto-mcp__cap_7","uri":"capability://data.processing.analysis.trace.export.and.report.generation","name":"trace export and report generation","description":"Exports trace analysis results in multiple formats (JSON, CSV, HTML reports) for integration with external tools and dashboards. Generates human-readable performance reports with charts, tables, and summary statistics. Supports custom report templates for different analysis types (CPU profiling, memory analysis, frame timing). Exports raw event data for further processing by downstream tools.","intents":["I need to export trace analysis results as JSON for integration with my monitoring dashboard","I want to generate a human-readable HTML report of performance issues for stakeholders","I need to export trace events as CSV for analysis in spreadsheet tools"],"best_for":["Teams integrating trace analysis into performance dashboards","Performance engineers sharing analysis results with non-technical stakeholders","Developers exporting trace data for further analysis in external tools"],"limitations":["HTML report generation requires template files — no built-in styling customization","Large trace exports (>100K events) may produce large JSON/CSV files (>100MB)","Report generation adds latency — complex reports may take several seconds to generate"],"requires":["Parsed trace analysis results","Optional: report templates for custom formatting","Output file system access for writing exports"],"input_types":["trace analysis results (metrics, events, statistics)","optional: report template specification","optional: export format preferences"],"output_types":["JSON structured data","CSV tabular data","HTML reports with charts","plain text summaries"],"categories":["data-processing-analysis","reporting"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Perfetto trace file (.pftrace or .pb format)","MCP client implementation (Claude, custom LLM agent, etc.)","Protobuf runtime library compatible with Perfetto schema","MCP client implementation (Claude, custom agent, etc.)","MCP server runtime (Node.js, Python, or language-specific MCP SDK)","Perfetto trace file accessible to the MCP server process","Parsed trace data in memory","Filter parameters (process ID, thread ID, category, time range, etc.)","Parsed trace data with sufficient event density","Configurable thresholds for anomaly detection (context switch rate, memory spike size, etc.)"],"failure_modes":["Limited to Perfetto binary format; does not support other trace formats (Chrome DevTools, Linux perf, etc.)","Protobuf schema must match the Perfetto version that generated the trace — schema mismatches cause parsing failures","No built-in trace filtering or aggregation — raw event extraction only; higher-level analysis requires post-processing","Memory usage scales linearly with trace file size; very large traces (>1GB) may cause memory pressure","Tool schemas must be manually defined and kept in sync with underlying trace parsing logic","Parameter validation is schema-based only; complex conditional logic requires custom validation code","No built-in caching of tool results — repeated queries re-parse the trace file","Filtering is done in-memory after full trace parsing — no index-based acceleration for large traces","Complex multi-criteria filters require sequential filtering passes, adding latency","No support for regex or pattern matching on event names or custom attributes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:34.639Z","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=antarikshc-perfetto-mcp","compare_url":"https://unfragile.ai/compare?artifact=antarikshc-perfetto-mcp"}},"signature":"fPt85gKDIA64SVWdxeEv7+ST0hNSmx6aOO/pioRUHgT/WzPH1vsfm1bVWtmZKUp8inOwzy4Iv7VD0DLh4+F3Bg==","signedAt":"2026-06-22T11:23:29.428Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/antarikshc-perfetto-mcp","artifact":"https://unfragile.ai/antarikshc-perfetto-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=antarikshc-perfetto-mcp","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"}}