| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs perfetto-mcp at 25/100. perfetto-mcp leads on ecosystem, while Supabase is stronger on quality.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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