| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements read-only access to memory bank files through MCP protocol with path traversal prevention and project-scoped file retrieval. Uses clean architecture layers (Presentation → Domain → Data Access → Infrastructure) to translate MCP read requests into filesystem operations, validating project and file paths against a root directory to prevent unauthorized access. Returns file contents as structured responses with error handling for missing or inaccessible files.
Unique: Implements project-scoped file access through clean architecture layers with explicit path validation at the Presentation layer, preventing directory traversal attacks while maintaining type-safe operations across domain, data access, and infrastructure layers — a pattern not typically found in simpler file-serving implementations
vs alternatives: Provides centralized, project-isolated memory access via MCP protocol whereas direct filesystem access or simple HTTP servers lack project boundaries and MCP integration
Enables creation of new memory bank files through MCP protocol with comprehensive path validation, project isolation, and file structure enforcement. The Presentation layer validates input parameters, the Domain layer enforces business rules (e.g., valid project and file paths), and the Infrastructure layer performs actual filesystem write operations. Prevents path traversal attacks by validating that resolved paths remain within the target project directory.
Unique: Validates file paths at multiple architectural layers (Presentation validates input format, Domain enforces business rules, Infrastructure performs resolved-path verification) rather than single-point validation, ensuring defense-in-depth against path traversal and invalid project references
vs alternatives: Safer than direct filesystem APIs or simple file servers because validation occurs across clean architecture layers with explicit project isolation, whereas alternatives typically validate only at entry point
Defines data access interfaces that abstract filesystem operations, allowing domain layer to request file operations without knowing implementation details. The Data Access layer specifies interfaces for read, write, update, and list operations, and the Infrastructure layer provides concrete filesystem implementations using Node.js fs module. This abstraction enables testing domain logic with mock implementations and potentially swapping filesystem for other storage backends (cloud storage, databases) without changing domain code.
Unique: Implements explicit data access interfaces rather than direct filesystem calls in domain logic, enabling mock implementations for testing and potential storage backend swapping without domain changes
vs alternatives: More testable than direct filesystem calls because domain logic depends on interfaces rather than concrete implementations, enabling mock-based unit testing without filesystem I/O
Implements concrete filesystem operations using Node.js fs module to fulfill data access layer interfaces, handling file reads, writes, updates, and directory listings with proper error handling and path resolution. Performs actual filesystem I/O, manages file permissions, and translates filesystem errors into domain-level error responses. Includes path resolution to normalize paths and prevent directory traversal, and handles edge cases like missing files, permission errors, and invalid paths.
Unique: Implements filesystem operations as concrete implementations of data access interfaces rather than scattered throughout application, enabling centralized error handling and potential future storage backend swapping
vs alternatives: More maintainable than scattered filesystem calls because all I/O is centralized in Infrastructure layer, whereas ad-hoc filesystem calls throughout the codebase are harder to test and modify
Configures memory bank root directory through MEMORY_BANK_ROOT environment variable, enabling deployment flexibility without code changes. The server reads this variable at startup to determine where all project directories are located, allowing different deployments (development, staging, production) to use different filesystem locations. Supports Docker deployment where the environment variable can be set via container environment or volume mounts.
Unique: Uses environment variable for configuration rather than config files or hardcoded paths, enabling containerized deployments and infrastructure-as-code patterns without code changes
vs alternatives: More flexible than hardcoded paths because environment variables enable different deployments to use different storage locations, whereas config files require per-environment copies
Defines type-safe operation schemas for each MCP tool with explicit input parameters, output types, and validation rules. Each operation specifies required parameters (project_id, file_path, contents), their types (string, etc.), and validation constraints. The Presentation layer validates incoming requests against these schemas before passing to domain logic, ensuring type safety and preventing invalid inputs from reaching business logic. Supports MCP tool definition format with parameter descriptions and types.
Unique: Implements explicit type-safe operation definitions in MCP tool schemas rather than implicit parameter handling, enabling compile-time type checking and runtime validation against defined schemas
vs alternatives: More robust than untyped parameter handling because schema definitions provide compile-time type checking and runtime validation, whereas ad-hoc parameter handling is error-prone
Provides in-place update capability for existing memory bank files through MCP protocol, replacing entire file contents while maintaining project isolation and path safety. Uses the same clean architecture pattern as file creation but targets existing files, with validation ensuring the file exists before update and the resolved path remains within project boundaries. Supports overwriting memory bank entries with new content from AI agents.
Unique: Distinguishes update from create operations at the Domain layer, enforcing existence checks before modification and using the same path validation infrastructure, providing semantic clarity that update is not idempotent with create
vs alternatives: Clearer semantics than generic write operations because it explicitly validates file existence and signals intent, whereas simple overwrite APIs don't distinguish between creation and modification
Lists all available projects in the memory bank root directory through MCP protocol, enabling clients to discover project structure without filesystem access. Implements read-only enumeration at the Presentation layer that queries the Infrastructure layer's filesystem operations to return project directories, with implicit filtering to exclude non-directory entries and hidden files. Supports multi-project management by allowing clients to discover which projects are available before accessing their files.
Unique: Implements project discovery as a dedicated MCP tool rather than embedding it in file operations, allowing clients to discover available projects before attempting file access — a pattern that improves UX for multi-project systems
vs alternatives: Provides explicit project discovery via MCP protocol whereas filesystem-based approaches require clients to understand directory structure or use separate APIs
+6 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 memory-bank-mcp at 36/100.
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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