| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Queries a pre-parsed AWS EC2 pricing catalogue to retrieve current instance pricing without making real-time API calls to AWS Pricing API. The catalogue is pre-indexed and stored locally or in-memory, enabling sub-100ms lookups across instance types, regions, and purchase options (on-demand, reserved, spot). Returns structured pricing data including hourly rates, vCPU counts, memory, and network performance metrics.
Unique: Uses pre-parsed AWS pricing catalogue instead of making real-time calls to AWS Pricing API, eliminating network latency and API rate-limiting concerns. The catalogue is indexed for fast lookups across instance types, regions, and purchase options, enabling sub-100ms query responses suitable for interactive tools and LLM agent decision-making.
vs alternatives: Faster and more reliable than querying AWS Pricing API directly because it trades real-time accuracy for deterministic, cached responses with no external dependencies or rate limits.
Exposes EC2 pricing data as a Model Context Protocol (MCP) server, allowing LLM agents, Claude, and other MCP-compatible clients to call pricing lookups as tools within their reasoning loops. Implements MCP resource and tool schemas to define pricing query parameters, validation rules, and response formats. Handles MCP protocol serialization, request routing, and error handling.
Unique: Implements MCP protocol as the primary integration layer, allowing seamless tool calling from Claude and other MCP clients without custom API wrappers. Uses MCP resource and tool schemas to define pricing queries with built-in validation and structured responses, enabling LLM agents to reason about costs as first-class decision factors.
vs alternatives: Tighter integration with Claude and MCP-based agents than REST APIs because it uses native MCP tool-calling semantics, reducing context overhead and enabling more natural agentic reasoning about pricing.
Supports querying and comparing EC2 pricing across multiple AWS regions and purchase options (on-demand, reserved, spot) in a single request. Returns structured comparison matrices showing price deltas, cost savings percentages, and breakeven analysis for reserved instances. Enables cost optimization analysis by surfacing regional arbitrage opportunities and purchase option trade-offs.
Unique: Provides structured comparison matrices across regions and purchase options in a single query, with built-in cost delta and savings calculations. Unlike AWS Pricing API which requires separate calls per region/option, this capability aggregates and normalizes data for direct comparison.
vs alternatives: More efficient than making multiple AWS Pricing API calls because it returns pre-computed comparison matrices with savings analysis, reducing client-side processing and enabling faster cost optimization decisions.
Implements a pre-parsing pipeline that fetches AWS pricing data (likely from AWS Pricing API or bulk export), parses it into an optimized in-memory or file-based index, and synchronizes the catalogue with a configurable refresh schedule. The pipeline handles AWS pricing data format transformations, deduplication, and indexing to enable sub-100ms lookups. Supports incremental updates to avoid full re-parsing on every refresh.
Unique: Implements a pre-parsing pipeline that transforms AWS pricing data into an optimized index, enabling sub-100ms lookups without real-time API calls. The pipeline handles format transformations, deduplication, and incremental updates to keep the catalogue fresh while minimizing processing overhead.
vs alternatives: More efficient than querying AWS Pricing API on-demand because it trades real-time accuracy for deterministic, indexed responses with no per-query latency or rate-limiting concerns.
Supports filtering EC2 instances by attributes (vCPU count, memory, network performance, processor type, architecture) and returns matching instance types with pricing. Implements attribute-based search logic that maps user-friendly filters to instance type specifications. Enables cost-aware instance selection by combining attribute constraints with pricing data.
Unique: Combines attribute-based filtering with pricing data to enable cost-aware instance selection. Maps user-friendly performance constraints (vCPU, memory, network) to instance type specifications and returns ranked results by price or performance.
vs alternatives: More efficient than manually comparing instances in AWS console because it returns filtered, ranked results with pricing in a single query, enabling faster decision-making for infrastructure planning.
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 AWS EC2 Pricing at 23/100.
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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
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