Supabase ranks higher at 42/100 vs @modelcontextprotocol/server-system-monitor at 22/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | @modelcontextprotocol/server-system-monitor | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 22/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Collects live CPU, memory, disk, and process-level metrics from the host operating system and exposes them through the Model Context Protocol (MCP) as callable tools. Uses native OS APIs (via Node.js child processes or system libraries) to poll system state at configurable intervals, then serializes metrics into structured JSON payloads that LLM clients can query synchronously or subscribe to via MCP's resource subscription mechanism.
Unique: Implements system monitoring as an MCP server rather than a standalone daemon or HTTP service, allowing LLM clients to query metrics directly via the MCP protocol without additional infrastructure; uses MCP's resource subscription pattern to enable push-based metric updates to clients that support it.
vs alternatives: Tighter integration with LLM workflows than traditional monitoring tools (Prometheus, Grafana) because metrics are callable tools in the agent's action space, not external dashboards; simpler deployment than containerized monitoring stacks because it runs as a single Node.js process.
Automatically generates MCP-compliant tool definitions (JSON schemas) for each system metric endpoint, enabling LLM clients to discover and call metric-fetching functions with proper type hints and descriptions. The server introspects available metrics at startup and generates OpenAPI-style schemas that describe input parameters (e.g., process filter, metric type) and output structures, which are then advertised via MCP's tools/list endpoint.
Unique: Generates MCP tool schemas dynamically from the server's metric collection logic rather than requiring manual schema authoring; integrates with MCP's tools/list and tools/call endpoints to provide full schema-driven function calling for system metrics.
vs alternatives: More discoverable than hardcoded metric endpoints because schemas are self-documenting and machine-readable; reduces friction compared to REST APIs where clients must read documentation to understand available metrics.
Breaks down system metrics to the individual process level, allowing LLM clients to query CPU, memory, and I/O usage per process, with optional filtering by process name, PID, or resource threshold. Internally uses Node.js child processes to invoke system commands (ps, top, or equivalent) and parses their output into structured process records, then applies filter logic to return only relevant processes.
Unique: Provides process-level granularity in an MCP context, enabling LLM agents to make decisions about specific processes rather than aggregate system metrics; uses command-line parsing to extract per-process data, making it lightweight compared to instrumenting individual processes.
vs alternatives: More granular than aggregate CPU/memory metrics because it attributes resources to specific processes; simpler than agent-side instrumentation (e.g., APM libraries) because it uses OS-level visibility without modifying target applications.
Allows configuration of how frequently the server collects system metrics (e.g., every 1 second, 5 seconds, or on-demand) and how long metrics are cached before being refreshed. Implements a polling loop that runs at a configurable interval, stores the latest snapshot in memory, and serves cached results to clients until the next poll cycle completes. Configuration is typically provided via environment variables or a config file at server startup.
Unique: Exposes polling interval as a configurable parameter rather than hardcoding it, allowing operators to tune the trade-off between metric freshness and CPU overhead; uses in-memory caching to avoid redundant system calls within a polling cycle.
vs alternatives: More flexible than fixed-interval monitoring because operators can adjust polling frequency without code changes; more efficient than on-demand polling for high-frequency queries because caching reduces system call overhead.
Implements MCP's resource subscription mechanism to enable clients to subscribe to metric updates and receive push-based notifications when metrics change, rather than polling. The server maintains a list of active subscriptions and pushes updated metric snapshots to subscribed clients at each polling interval or when metrics exceed configured thresholds. Uses MCP's resources/subscribe and resources/updated endpoints to manage subscriptions and deliver updates.
Unique: Leverages MCP's resource subscription protocol to provide push-based metric delivery instead of relying solely on polling; enables efficient multi-client metric distribution by centralizing subscription management in the server.
vs alternatives: Lower latency than polling-based approaches because clients receive updates immediately; more efficient than individual polling because the server broadcasts to all subscribers in a single operation.
Collects and exposes disk-level metrics including I/O throughput (read/write bytes per second), I/O operations per second (IOPS), disk utilization percentage, and available/used space per filesystem. Internally queries the OS filesystem APIs (via df, iostat, or equivalent) and parses output into structured disk metrics, optionally tracking I/O deltas between polling intervals to compute throughput.
Unique: Combines filesystem capacity metrics with I/O performance metrics in a single capability, providing both storage health (utilization) and performance (throughput/IOPS) visibility; computes I/O deltas across polling intervals to derive throughput without requiring external profiling tools.
vs alternatives: More comprehensive than simple disk space checks because it includes I/O performance metrics; more accessible than kernel-level profiling tools (perf, blktrace) because it uses standard OS utilities.
Collects network interface statistics including bytes sent/received, packet counts, error rates, and optionally tracks active network connections (TCP/UDP sockets) with their associated processes. Queries OS network APIs (via ifconfig, netstat, ss, or equivalent) and parses output into structured network metrics, optionally computing throughput deltas between polling intervals.
Unique: Combines interface-level throughput metrics with process-level connection tracking, enabling agents to correlate network activity with specific applications; computes throughput deltas to provide real-time bandwidth visibility without external tools.
vs alternatives: More actionable than raw interface stats because it includes process attribution; simpler than packet-level analysis (tcpdump, Wireshark) because it uses OS-level socket APIs.
Provides detailed memory usage breakdown including resident set size (RSS), heap usage, external memory, and optionally distinguishes between different memory types (physical, swap, cached). On Linux, parses /proc/meminfo and /proc/[pid]/status for detailed memory accounting; on other OSes, uses available APIs to approximate breakdown. Exposes both system-wide memory and per-process memory details.
Unique: Provides detailed memory breakdown (RSS, heap, external) rather than just total memory usage, enabling agents to diagnose memory issues; uses OS-specific APIs (/proc on Linux) to access detailed memory accounting without requiring process instrumentation.
vs alternatives: More diagnostic than simple memory percentage because it breaks down memory by type; more accessible than language-specific profilers because it works across processes regardless of implementation language.
+1 more capabilities
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 @modelcontextprotocol/server-system-monitor at 22/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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
+1 more capabilities