| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Analyzes CAP (Cloud Application Programming) project structure to extract data models, service definitions, and configuration metadata. Implements filesystem-based AST parsing of CDS (Core Data Services) files to build a semantic representation of the application architecture, enabling AI models to understand domain entities, relationships, and service boundaries without manual documentation.
Unique: Purpose-built for SAP CAP ecosystem — parses CDS syntax natively and maps to CAP's specific service and entity model, rather than generic code analysis. Integrates directly with CAP's configuration system to understand project layout conventions.
vs alternatives: Unlike generic code indexing tools, this MCP server understands CAP-specific patterns (aspects, compositions, service definitions) and can expose them to LLMs in a semantically meaningful way for domain-aware code generation.
Implements the Model Context Protocol (MCP) server specification to register CAP-specific resources (data models, services, configurations) and tools (code generators, validators, query builders) as callable functions within AI client contexts. Uses MCP's resource URI scheme and tool JSON-Schema definitions to create a standardized interface that allows Claude and other MCP-compatible clients to discover and invoke CAP development capabilities.
Unique: Implements MCP server specification for CAP domain — defines CAP-specific resource types (entities, services, configurations) and tool schemas that map to CAP development workflows, rather than generic tool registration.
vs alternatives: Tighter integration with CAP than generic MCP servers — understands CAP's service model, entity relationships, and development patterns, allowing more intelligent tool suggestions and resource navigation.
Generates CDS entity definitions, service implementations, and configuration boilerplate based on natural language descriptions or schema templates. Uses LLM context (via MCP) to understand existing project patterns and generates code that follows the project's conventions, naming standards, and architectural patterns. Integrates with the project's schema introspection to ensure generated code is compatible with existing entities and services.
Unique: Leverages project-specific schema introspection to generate code that respects existing naming conventions, association patterns, and service structure — not generic boilerplate, but context-aware generation.
vs alternatives: Unlike generic code generators, this capability understands CAP's CDS syntax and can generate code that integrates seamlessly with existing entities and services by analyzing the project's actual structure.
Validates CDS file syntax and semantic correctness (entity definitions, associations, service definitions, annotations) and reports errors with precise line numbers and remediation suggestions. Implements a CDS parser that checks for common mistakes (circular associations, undefined entity references, invalid annotations) and provides actionable error messages that can be displayed in the AI client or IDE.
Unique: CDS-specific validator that understands CAP's entity model, association rules, and annotation semantics — not a generic syntax checker, but domain-aware validation.
vs alternatives: Provides CAP-specific error messages and suggestions (e.g., 'Association must reference a valid entity' with the actual entity name) rather than generic parser errors.
Maintains and exposes project context (schema, services, configurations, recent files) to the LLM through MCP resources, enabling the AI to make informed suggestions without requiring developers to manually paste code snippets. Implements a context indexing system that tracks project structure changes and updates the available resources dynamically, allowing the LLM to reference current project state in its responses.
Unique: Implements project-aware context indexing specific to CAP structure — understands db/, srv/, and app/ directory conventions and exposes them as queryable MCP resources rather than requiring manual context assembly.
vs alternatives: Automatically maintains project context without developer intervention, unlike manual context passing or generic code indexing tools that don't understand CAP's specific directory and file conventions.
Analyzes CAP service definitions to discover exposed endpoints, their request/response schemas, and authentication requirements. Generates documentation (OpenAPI/Swagger-compatible format or markdown) that describes available services, entities, and operations, making it easy for AI assistants to understand and suggest correct API usage patterns.
Unique: Extracts endpoint definitions from CAP's CDS service syntax and generates documentation that reflects CAP's specific service model (entity exposure, CRUD operations, custom actions) rather than generic API analysis.
vs alternatives: Understands CAP's service definition patterns and can generate accurate endpoint documentation without requiring manual OpenAPI specifications or external API documentation tools.
Provides a standardized MCP interface that allows any MCP-compatible LLM client (Claude, Cline, custom agents) to interact with CAP development tools and project context. Abstracts away provider-specific details and uses MCP's protocol to ensure compatibility across different AI platforms and clients without requiring provider-specific SDKs or integrations.
Unique: Implements MCP as a protocol abstraction layer for CAP development — allows any MCP-compatible client to access CAP tools without provider-specific code, enabling true interoperability.
vs alternatives: Unlike provider-specific integrations (e.g., Claude plugins, Copilot extensions), MCP provides a vendor-neutral protocol that works across multiple AI platforms and clients.
Generates CDS Query Language (CQL) queries and OData requests based on natural language descriptions or schema context. Understands entity relationships, filters, projections, and aggregations, and generates syntactically correct queries that can be executed against CAP's data layer. Validates generated queries against the project's schema to ensure they reference valid entities and properties.
Unique: Generates queries that respect CAP's entity model and CQL syntax — understands associations, compositions, and CAP-specific query semantics rather than generic SQL generation.
vs alternatives: Produces CAP-native queries (CQL/OData) that integrate seamlessly with CAP's data layer, unlike generic SQL generators that would require translation or custom adapters.
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 @cap-js/mcp-server at 34/100. @cap-js/mcp-server leads on adoption and 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
+1 more capabilities