perfetto-mcp vs AWS MCP Servers

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.

Unique: Bridges Perfetto's native binary trace format directly into MCP's tool-calling interface, allowing LLMs to query performance traces without UI interaction. Uses protobuf deserialization to maintain fidelity with Perfetto's internal event representation, enabling structured analysis of CPU scheduling, memory, and GPU events in a single unified interface.

vs alternatives: Unlike manual Perfetto UI inspection or custom Python scripts, perfetto-mcp exposes trace analysis as standardized MCP tools that any LLM client can invoke, enabling automated performance debugging workflows and trace-driven agent reasoning.

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.

Unique: Implements MCP server protocol to expose Perfetto trace analysis as first-class tools, using MCP's schema-based function registry to enable LLMs to discover and invoke trace queries. Handles the full MCP lifecycle: tool registration, parameter validation, invocation routing, and response serialization.

vs alternatives: Compared to REST APIs or custom Python libraries, MCP tool registration provides native LLM integration with zero client-side boilerplate — Claude and other MCP clients can invoke trace analysis directly without custom API wrappers or authentication logic.

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.

Unique: Provides in-memory filtering of parsed Perfetto events with support for multi-dimensional criteria (process, thread, category, time range). Implements sequential filtering passes to handle complex queries without requiring a separate indexing layer or database.

vs alternatives: Simpler than building a full trace database or index, but slower than indexed queries — suitable for interactive analysis of medium-sized traces where latency is acceptable but complexity must be minimized.

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.

Unique: Generates trace summaries through single-pass aggregation of parsed events, providing LLMs with structured metadata (process/thread lists, event counts, duration) without requiring full event enumeration or complex queries.

vs alternatives: Faster than iterating through all events manually, but less detailed than full trace analysis — ideal for initial trace assessment and LLM context building before deeper investigation.

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.

Unique: Implements heuristic-based anomaly detection directly on parsed Perfetto events, flagging performance issues (context switches, memory spikes, blocking operations) without requiring external ML models or statistical baselines. Exposes anomalies as structured results for LLM reasoning.

vs alternatives: Simpler and faster than ML-based anomaly detection, but less accurate for subtle or workload-specific issues — suitable for automated screening and LLM-driven investigation where false positives are acceptable.

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).

Unique: Correlates CPU and GPU events from Perfetto traces to identify frame timing bottlenecks, distinguishing between GPU stalls and CPU-GPU synchronization delays. Implements frame-based aggregation of GPU work with per-frame latency attribution.

vs alternatives: Provides programmatic frame timing analysis compared to Perfetto UI's manual frame inspection, enabling automated jank detection and integration with performance monitoring systems.

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.

Unique: Implements trace-based regression detection with statistical significance testing, enabling automated performance regression detection in CI/CD pipelines. Computes delta metrics across multiple dimensions (CPU, memory, GPU) with per-component attribution.

vs alternatives: Provides automated regression detection compared to manual trace comparison, and integrates with CI/CD systems for continuous performance monitoring.

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.

Unique: Generates multi-format exports of trace analysis results with support for custom report templates, enabling integration with external dashboards and sharing with non-technical stakeholders. Implements efficient serialization for large trace datasets.

vs alternatives: Provides programmatic export compared to Perfetto UI's manual screenshot/export, enabling automated report generation and integration with monitoring systems.

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awslabs/mcp | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki awslabs/mcp Index your code with Devin Edit Wiki Share Loading... Last indexed: 8 January 2026 ( 49d158 ) Overview What is Model Context Protocol? Available MCP Servers Server Workflow Classifications Architecture System Design Client-Server Interaction Package Structure & Dependencies Security & Permission Model Documentation System Core Infrastructure Core MCP Server AWS API MCP Server Lambda Handler & Remote Servers Infrastructure as Code Servers AWS IaC MCP Server Terraform MCP Server CDK MCP Server CloudFormation & Cloud Control Servers Container & Compute Servers ECS MCP Server EKS & Kubernetes Servers Lambda Tool MCP Server Serverless & Container Tools AI & Machine Learning Servers Bedrock KB Retrieval MCP Server Nova Canvas MCP Server SageMaker AI MCP Server AWS HealthOmics MCP Server Bedrock AgentCore & Other AI Servers Data & Analytics Servers DynamoDB MCP Server PostgreSQL MCP Server Other Database Servers S3 Tables & Storage Servers Analytics & Data Processing Servers Operations & Monitoring Serv