| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Evaluates and ranks embedding models across standardized benchmarks using the MTEB (Massive Text Embedding Benchmark) framework, which tests models on 56+ diverse tasks spanning retrieval, clustering, semantic similarity, and reranking. The leaderboard aggregates performance metrics across these task categories and computes composite scores, enabling direct comparison of model quality across different architectures, sizes, and training approaches. Results are persisted in a structured database and visualized in real-time as new model submissions are processed.
Unique: MTEB is the largest standardized benchmark for embedding models with 56+ diverse tasks across 112 datasets, using a unified evaluation protocol that enables fair comparison across model families (dense, sparse, cross-encoder) and training approaches (supervised, unsupervised, domain-specific fine-tuning). The leaderboard integrates directly with HuggingFace Hub for seamless model submission and uses containerized evaluation (Docker) to ensure reproducibility and isolation.
vs alternatives: More comprehensive and standardized than ad-hoc benchmarks or single-task evaluations; provides task-specific breakdowns that reveal model strengths/weaknesses, whereas competitors like BEIR focus only on retrieval tasks
Accepts model submissions via HuggingFace Hub integration and automatically queues them for evaluation against the full MTEB benchmark suite using a containerized evaluation environment. The pipeline orchestrates model loading, task execution, result aggregation, and leaderboard ranking updates without manual intervention. Submissions are processed asynchronously with status tracking and result persistence to enable reproducible, auditable evaluation runs.
Unique: Uses HuggingFace Hub as the submission interface and model registry, eliminating the need for separate model uploads or API credentials. Evaluation runs in isolated Docker containers with pinned dependencies to ensure reproducibility across all submissions, and results are automatically synced back to the model's Hub page.
vs alternatives: Simpler submission workflow than custom evaluation APIs because it leverages existing HuggingFace Hub infrastructure; more reproducible than manual evaluation because containerization eliminates environment drift
Provides a web-based interface for exploring benchmark results with dynamic filtering by model properties (model size, training approach, language support), task categories (retrieval, clustering, semantic similarity), and performance metrics. Sorting enables ranking by composite score, task-specific performance, or metadata attributes. The interface is built as a Gradio/Streamlit app deployed on HuggingFace Spaces with client-side filtering for responsive interaction.
Unique: Leaderboard filtering is implemented client-side using Gradio/Streamlit's reactive state management, enabling instant filter updates without server round-trips. The interface exposes task-specific breakdowns (e.g., retrieval@k, clustering NMI) alongside composite scores, allowing users to identify models optimized for their specific task.
vs alternatives: More interactive and exploratory than static leaderboard tables; client-side filtering provides instant feedback compared to server-side filtering with page reloads
Decomposes overall model performance into granular task-specific metrics across 56+ MTEB tasks, organized by category (retrieval, clustering, semantic similarity, reranking, etc.). For each task, the leaderboard displays metric-specific scores (e.g., NDCG@10 for retrieval, NMI for clustering) and percentile rankings relative to other models. This enables identification of model strengths and weaknesses across different embedding use cases.
Unique: MTEB organizes tasks into semantic categories (retrieval, clustering, semantic similarity, reranking, etc.) and exposes task-specific metrics (NDCG@10, MRR, NMI, Spearman correlation) rather than a single composite score. The leaderboard displays percentile rankings for each task, enabling users to identify models that are strong/weak on specific task types relative to the full model population.
vs alternatives: More granular than single-score benchmarks; enables task-specific model selection whereas competitors like BEIR provide only retrieval metrics
Captures and displays model metadata (architecture, training approach, model size, language support, license) alongside benchmark results, enabling reproducibility and informed model selection. Metadata is extracted from HuggingFace model cards and evaluation logs, and linked to the model's Hub page for full transparency. This enables users to understand the context of benchmark results and reproduce evaluations if needed.
Unique: Metadata is sourced directly from HuggingFace model cards and evaluation logs, creating a single source of truth linked to the authoritative model repository. The leaderboard displays evaluation metadata (MTEB version, evaluation date, environment) alongside model metadata, enabling reproducibility and version tracking.
vs alternatives: More transparent than proprietary benchmarks because all metadata and evaluation details are publicly visible; integration with HuggingFace Hub ensures metadata is kept in sync with authoritative model information
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 leaderboard at 20/100. leaderboard leads on 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
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