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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Persists user memories with semantic embeddings and structured metadata formatting, enabling later retrieval by meaning rather than keyword matching. Implements a vector-backed storage layer that captures memory content, context, and relationships, allowing memories to be organized with custom schemas and formatting templates that preserve both raw content and semantic meaning for downstream search operations.
Unique: Combines MCP protocol integration with semantic embeddings and structured formatting in a single server, allowing Cline to save and organize memories with both vector-based retrieval and schema-based validation without requiring separate infrastructure
vs alternatives: Tighter integration with Cline's workflow than generic vector databases, with built-in formatting templates that reduce boilerplate for memory organization
Retrieves memories by semantic similarity rather than exact keyword matching, using vector embeddings to find contextually relevant memories even when search queries use different phrasing or terminology. Implements approximate nearest-neighbor search over the memory embedding space, allowing developers to query memories by intent, topic, or concept rather than requiring exact recall of how the memory was originally phrased.
Unique: Operates as an MCP tool within Cline's context, enabling semantic search directly in the code editor workflow without context-switching to a separate search interface or database tool
vs alternatives: More integrated than standalone vector databases for developer workflows, with direct MCP bindings that reduce latency and context loss compared to REST API calls
Applies structured formatting templates and schema validation to memories at save time, ensuring consistent organization and enabling structured queries. Implements a schema-based validation layer that enforces field presence, type correctness, and format compliance, allowing memories to be organized by custom categories, tags, and metadata fields defined by the user or application.
Unique: Combines schema validation with semantic storage in a single MCP tool, allowing developers to enforce data consistency while maintaining semantic searchability without separate validation infrastructure
vs alternatives: Tighter integration than using separate validation libraries, with schema enforcement built into the memory persistence layer rather than requiring post-hoc validation
Exposes memory operations (save, search, format) as MCP tools that Cline can invoke directly within its agentic workflow, using the Model Context Protocol to standardize tool definitions, request/response schemas, and error handling. Implements MCP server endpoints that register memory tools with Cline's tool registry, allowing the AI assistant to autonomously decide when to save context, retrieve relevant memories, or format information without explicit user prompting.
Unique: Implements Memory Box as a first-class MCP server rather than a plugin or extension, allowing Cline to treat memory operations as native tools with standardized schemas and error handling
vs alternatives: More standardized than custom Cline plugins, with MCP protocol ensuring compatibility across different MCP clients and reducing vendor lock-in
Retrieves memories contextually relevant to the current task or conversation, using the agent's current state (file being edited, conversation history, task description) to filter and rank memory results. Implements context-aware retrieval by combining semantic similarity with task-specific metadata filtering, allowing the agent to surface the most relevant memories without explicit user queries.
Unique: Combines semantic search with task-aware filtering, allowing the MCP server to proactively surface relevant memories based on Cline's current context rather than requiring explicit search queries
vs alternatives: More proactive than manual memory search, with automatic context inference reducing cognitive load on developers compared to manually querying for relevant past decisions
Enables querying memories across multiple dimensions (semantic content, tags, timestamps, source context) with combined filtering and ranking. Supports complex queries that filter by metadata (date ranges, tags, source) while simultaneously performing semantic search, returning results ranked by relevance across all dimensions rather than simple keyword matching.
Unique: Combines semantic search with structured metadata filtering in a single query operation, avoiding the need for separate semantic and keyword searches. Ranks results across both dimensions rather than treating them as separate result sets.
vs alternatives: More powerful than semantic-only search because it enables precise filtering, and more intuitive than boolean query languages because it combines semantic and structured search naturally
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 Box MCP Server at 28/100.
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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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