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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) as a standardized bridge layer that translates MCP client requests into EduBase REST API calls, exposing over 160 educational platform operations through a unified tool registry with naming convention edubase_<method>_<endpoint>. The server uses @modelcontextprotocol/sdk v1.12.3 for protocol compliance and maintains bidirectional communication between AI assistants and the EduBase backend.
Unique: Exposes 160+ EduBase operations through standardized MCP tool registry with consistent naming convention, enabling AI clients to discover and invoke educational platform capabilities without custom integration code
vs alternatives: Provides MCP-native access to EduBase compared to raw REST API clients, reducing integration complexity for LLM-based applications while maintaining full platform feature parity
Manages atomic educational questions through 8 dedicated tools (edubase_get_question, edubase_post_question, etc.) that support parametrization for infinite question variations, multiple question types (TEXT, CHOICE, NUMERIC, MATRIX), LaTeX typesetting for STEM content, and automatic grading. Questions serve as the foundational building block in the three-tier content hierarchy and can be organized into Quiz Sets and Exams.
Unique: Supports parametrized questions with infinite variations and LaTeX typesetting through MCP tools, enabling AI systems to generate and manage adaptive assessments without direct platform access
vs alternatives: Provides parametrization and STEM support through MCP compared to static question banks in typical LMS systems, enabling dynamic assessment generation at scale
Implements flexible transport mechanisms supporting multiple deployment architectures including stdio (local process), HTTP/WebSocket (remote server), and Docker containerization. The transport layer abstracts communication between MCP clients and the EduBase server, enabling deployment in various environments (local development, cloud, on-premise) with configurable authentication and rate limiting.
Unique: Supports multiple transport mechanisms (stdio, HTTP, WebSocket) and deployment options (local, Docker, Smithery) enabling flexible MCP server deployment across development and production environments
vs alternatives: Provides multiple deployment options compared to single-transport MCP servers, enabling flexible infrastructure choices and scaling strategies
Implements authentication system managing EduBase API credentials (App ID and Secret Key) obtained from the EduBase dashboard integrations menu. Credentials are used to authenticate all MCP tool requests to the EduBase platform, with support for token-based authentication and optional SSO integration for enterprise deployments.
Unique: Manages EduBase API credentials with support for SSO integration, enabling secure authentication of MCP requests to the educational platform
vs alternatives: Provides credential management with SSO support compared to basic API key handling, enabling enterprise-grade authentication and audit capabilities
Implements rate limiting and request throttling mechanisms to protect the EduBase platform from excessive API usage and ensure fair resource allocation across MCP clients. Rate limits are applied at the server level with configurable thresholds and backoff strategies.
Unique: Implements server-level rate limiting to protect EduBase platform resources, enabling controlled API access across multiple MCP clients
vs alternatives: Provides built-in rate limiting compared to uncontrolled API access, enabling resource protection and fair allocation in multi-client deployments
Exposes 7 tools for Quiz Set management (edubase_get_quiz_set, edubase_post_quiz_set, etc.) that enable organizing questions into reusable collections serving as the middle organizational layer in the three-tier content hierarchy. Quiz Sets group related questions and can be composed into Exams, supporting content reuse and modular assessment design.
Unique: Implements middle-tier organizational layer in three-tier content hierarchy (Questions → Quiz Sets → Exams), enabling modular assessment design and question reuse through MCP tools
vs alternatives: Provides explicit quiz set composition layer compared to flat question banks, enabling better content organization and reuse patterns in large-scale educational systems
Manages secure, time-limited assessment instances through 8 dedicated tools (edubase_get_exam, edubase_post_exam, etc.) built from Quiz Sets with integrated cheating detection capabilities. Exams represent the top tier of the three-tier content hierarchy and enforce security controls including time limits, access restrictions, and proctoring integration for high-stakes assessments.
Unique: Integrates cheating detection and security controls at the exam level within the three-tier hierarchy, enabling AI systems to manage secure assessments with built-in integrity monitoring
vs alternatives: Provides native cheating detection and security controls compared to basic quiz platforms, enabling high-stakes assessment use cases through MCP integration
Exposes 15 user management tools (edubase_get_user, edubase_post_user, etc.) for creating, updating, and managing user accounts within the EduBase platform. Integrates with SSO systems for enterprise authentication and maintains user profiles with role assignments, organizational membership, and permission associations.
Unique: Provides programmatic user management with SSO integration through MCP tools, enabling AI systems to manage educational platform identities without direct database access
vs alternatives: Offers MCP-native user management compared to manual admin panels, enabling automated user provisioning and SSO integration at scale
+5 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 EduBase at 27/100.
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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