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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Bridges AI agents (Claude Desktop, Cursor, Windsurf) directly to Last9 observability platform using the Model Context Protocol, enabling LLMs to query live production logs, metrics, traces, and alerts without context switching. Implements a dual-transport architecture (HTTP for managed mode, STDIO for local/air-gapped) that translates natural language intent into structured Last9 API calls, with background attribute caching to optimize LLM token usage and reduce round-trip latency.
Unique: Implements dual-transport MCP server (HTTP + STDIO) with background attribute caching and chunking strategy specifically optimized for LLM token efficiency, enabling agents to maintain context across multi-turn debugging sessions without exhausting context windows. Translates natural language to Last9's JSON-pipeline query syntax automatically.
vs alternatives: Unlike generic observability dashboards or REST API clients, Last9 MCP embeds production context directly into the LLM's reasoning loop with zero IDE context-switching, and optimizes for token efficiency through intelligent result chunking and attribute discovery.
Exposes high-level service summaries and RED metrics (Rate, Error, Duration) through structured MCP tools that execute PromQL queries against Last9's metrics backend. Abstracts Prometheus query complexity by providing pre-built metric templates while allowing raw PromQL execution for advanced use cases, with automatic time-range normalization and result formatting for LLM consumption.
Unique: Provides both templated RED metric queries (for simplicity) and raw PromQL execution (for flexibility), with automatic time-range normalization and LLM-optimized result formatting. Maintains an internal attribute cache to enable service/metric discovery without requiring users to know exact label names.
vs alternatives: Simpler than direct Prometheus API access (no PromQL expertise required for common queries) but more flexible than static dashboards, allowing LLMs to dynamically construct queries based on incident context.
Generates contextual deep links to Last9 UI that preserve query parameters (service, time range, filters) enabling users to seamlessly transition from LLM-assisted analysis to manual investigation. Links include pre-filled filters, time ranges, and service selections, reducing manual re-entry of context. Supports links to logs, metrics, traces, and alerts views.
Unique: Generates context-preserving deep links that encode query parameters (service, time range, filters) into Last9 UI URLs, enabling seamless transition from LLM analysis to manual investigation without re-entering context.
vs alternatives: More useful than generic Last9 links (preserves query context) and more maintainable than hard-coded UI paths (parameterized link generation adapts to UI changes).
Manages two authentication modes: API Token for HTTP mode (long-lived, suitable for service accounts) and Refresh Token for STDIO mode (short-lived, suitable for user sessions). Implements token validation, expiration handling, and secure credential storage. Abstracts authentication differences between modes, allowing same tool implementations to work with either credential type.
Unique: Implements dual authentication modes (API Token for HTTP, Refresh Token for STDIO) with automatic token refresh and expiration handling, abstracting auth differences while maintaining security best practices.
vs alternatives: More flexible than single-auth systems (supports both service and user authentication) and more secure than hardcoded credentials (supports environment variables and credential rotation).
Enables LLMs to query logs using Last9's JSON-pipeline filter syntax, with automatic attribute discovery that surfaces available log fields and their cardinality. Implements a chunking strategy to handle large result sets, manages drop-rule configuration for sensitive data filtering, and generates deep links to Last9 UI for manual log exploration. Abstracts complex log query DSL through structured tool parameters while exposing raw query capability for advanced filtering.
Unique: Combines templated log queries (for common patterns) with raw JSON-pipeline DSL support, includes automatic attribute discovery to enable dynamic query construction, and implements chunking strategy optimized for LLM token budgets. Manages drop-rule visibility to help teams understand data filtering policies.
vs alternatives: More powerful than simple keyword search (supports complex multi-field filtering) but more accessible than raw Elasticsearch/Loki queries; attribute discovery enables LLMs to construct valid queries without prior knowledge of log schema.
Retrieves distributed traces by trace ID or service name, with automatic exception aggregation across trace spans. Implements span-level filtering, service dependency visualization, and correlation of trace data with deployment events. Generates structured trace summaries optimized for LLM analysis, including root cause indicators and latency attribution across service boundaries.
Unique: Automatically aggregates exceptions across trace spans and correlates with deployment events, providing root-cause indicators without requiring manual trace analysis. Implements span-level filtering and service dependency visualization derived from trace topology.
vs alternatives: More structured than raw trace JSON (includes exception aggregation and latency attribution), and integrates deployment context to enable correlation analysis that standalone tracing tools don't provide.
Exposes firing alerts and system change events (deployments, configuration changes) through structured MCP tools, enabling LLMs to correlate alert triggers with recent infrastructure changes. Implements event timeline visualization and alert metadata enrichment, allowing agents to construct incident narratives by linking alerts to deployment events and metric anomalies.
Unique: Automatically correlates firing alerts with deployment and configuration change events, enabling LLMs to construct incident narratives without manual timeline assembly. Enriches alert metadata with context about what changed recently, surfacing potential root causes.
vs alternatives: More contextual than alert-only systems (includes change events for correlation) and more actionable than change logs alone (links changes to their observable impact via alerts and metrics).
Implements the Model Context Protocol tool registration system with a background attribute cache that discovers and maintains available log fields, metric labels, and service names. Dynamically updates tool schemas based on cached attributes, enabling LLMs to construct valid queries without prior knowledge of data structure. Handles tool lifecycle (registration, discovery, invocation) and maintains an internal state machine for cache synchronization.
Unique: Implements background attribute caching with automatic tool schema updates, enabling MCP clients to discover and invoke tools with current data structure without manual configuration. Maintains internal state machine for cache lifecycle and synchronization.
vs alternatives: More dynamic than static tool definitions (adapts to schema changes automatically) and more efficient than querying attributes on every invocation (background caching reduces latency and API calls).
+4 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 Last9 at 27/100.
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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
+1 more capabilities