| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Integrates with AWS CDK Nag to analyze Infrastructure-as-Code constructs against prescriptive security and best-practice rules, returning violations with suppression metadata. The MCP server wraps CDK Nag's rule engine to expose compliance checks through a standardized tool interface, enabling LLM agents to validate CDK stacks without direct CLI invocation and to understand rule suppression contexts.
Unique: Exposes CDK Nag rule evaluation through MCP's tool-calling interface, allowing LLM agents to reason about compliance violations and suppressions without spawning CLI processes; integrates suppression metadata to help agents understand why rules are disabled and whether they're properly justified.
vs alternatives: Provides programmatic, agent-friendly access to CDK Nag rules with suppression context, whereas direct CDK Nag CLI usage requires parsing text output and lacks structured suppression reasoning.
Leverages AWS Solutions Constructs patterns and CDK best practices to generate architectural recommendations for infrastructure code. The server analyzes CDK constructs and synthesized CloudFormation to suggest higher-level construct patterns, security hardening, and cost optimization strategies, returning guidance as structured recommendations that LLM agents can reason about and apply.
Unique: Integrates AWS Solutions Constructs pattern library directly into MCP tool interface, enabling LLM agents to discover and reason about higher-level construct patterns without manual documentation lookup; provides structured, actionable recommendations tied to specific construct patterns and security/cost implications.
vs alternatives: Offers programmatic access to Solutions Constructs guidance with structured output suitable for agent reasoning, whereas manual documentation review or generic CDK tutorials lack pattern-specific, context-aware recommendations.
Indexes and exposes the AWS Solutions Constructs library patterns through MCP, enabling agents to discover available constructs, their properties, and generated Bedrock Agent schemas. The server maintains a queryable catalog of construct patterns (e.g., api-lambda, s3-lambda) with metadata about use cases, security defaults, and configuration options, and can generate structured schemas for use in Bedrock Agent tool definitions.
Unique: Maintains a queryable, MCP-exposed catalog of AWS Solutions Constructs patterns with automatic Bedrock Agent schema generation, allowing agents to discover and reason about construct patterns without manual documentation parsing or schema hand-coding.
vs alternatives: Provides programmatic, agent-friendly pattern discovery with auto-generated Bedrock schemas, whereas consulting AWS documentation or construct source code requires manual schema creation and lacks structured discoverability.
Analyzes CDK Nag rule suppressions to verify they are properly documented and justified, enforcing organizational policies around suppression usage. The server inspects suppression metadata (reason, justification, expiration) and can flag suppressions that lack documentation, are expired, or violate suppression policies, enabling governance of infrastructure code quality.
Unique: Implements configurable suppression validation policies that can be enforced through MCP, enabling organizations to govern suppression usage programmatically rather than through manual code review; integrates with CDK Nag metadata to track suppression justifications and expiration.
vs alternatives: Provides automated, policy-driven suppression validation through MCP, whereas manual code review or generic linting tools lack suppression-specific governance and cannot enforce organizational policies.
Exposes CDK construct internals through MCP by parsing synthesized CloudFormation and construct metadata to extract properties, dependencies, and configuration details. The server can introspect any CDK construct (L1, L2, or L3) to return its synthesized resources, property values, and relationships, enabling agents to understand and reason about infrastructure topology without direct code analysis.
Unique: Provides MCP-exposed introspection of CDK constructs by parsing synthesized CloudFormation and construct metadata, allowing agents to understand infrastructure topology and configuration without parsing TypeScript/Python code or invoking CDK CLI directly.
vs alternatives: Enables programmatic construct introspection through MCP with structured output suitable for agent reasoning, whereas manual code review or CDK CLI commands (cdk synth) require parsing and lack agent-friendly structure.
Generates CDK infrastructure code in TypeScript or Python using AWS Solutions Constructs patterns and best practices, guided by natural language descriptions or architectural specifications. The server synthesizes construct instantiation code with proper configuration, security defaults, and error handling, producing production-ready code snippets that agents can suggest or directly apply to CDK projects.
Unique: Generates CDK code in multiple languages (TypeScript/Python) using Solutions Constructs patterns with embedded security defaults and best practices, producing agent-friendly code suggestions that can be directly integrated into CDK projects without manual refinement.
vs alternatives: Provides pattern-aware, multi-language CDK code generation through MCP, whereas generic code generation tools or manual construct documentation require developers to hand-code boilerplate and security configurations.
Analyzes CDK stack definitions to extract and visualize dependencies between constructs, stacks, and external resources, returning structured dependency graphs and cross-stack references. The server parses CDK code or synthesized CloudFormation to identify import/export relationships, parameter passing, and resource dependencies, enabling agents to understand infrastructure topology and detect circular dependencies or missing references.
Unique: Provides MCP-exposed static analysis of CDK stack dependencies with structured graph output, enabling agents to reason about infrastructure topology and detect issues without manual code review or CloudFormation parsing.
vs alternatives: Offers programmatic dependency analysis through MCP with structured output suitable for agent reasoning and visualization, whereas manual code review or AWS console inspection lacks automated detection and structured output.
Manages and resolves CDK context values (availability zones, AMI IDs, VPC information) through MCP, enabling agents to query context, set context values, and understand context dependencies. The server interfaces with CDK's context system to retrieve cached values, query AWS for dynamic values, and manage context.json files, allowing agents to ensure context is properly resolved before synthesis.
Unique: Exposes CDK context management through MCP, allowing agents to query, set, and resolve context values programmatically without direct file system or AWS API calls; integrates with CDK's context caching and dynamic resolution mechanisms.
vs alternatives: Provides programmatic context management through MCP, whereas manual context.json editing or CDK CLI commands require file system access and lack agent-friendly interfaces.
+2 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 AWS CDK at 26/100.
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