| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes code generation models against a curated benchmark suite using automated test execution and pass/fail scoring. The system runs submitted model outputs through functional correctness tests, measuring performance across multiple code generation tasks with standardized metrics (pass@1, pass@10, etc.). Integration with HuggingFace Model Hub enables direct model loading and evaluation without manual setup.
Unique: Integrates directly with HuggingFace Model Hub for seamless model loading and evaluation, using automated test execution against a curated code generation benchmark suite with standardized pass@k metrics rather than manual evaluation or subjective scoring
vs alternatives: Provides public, reproducible benchmarking for code generation models with lower barrier to entry than custom evaluation infrastructure, though less flexible than self-hosted evaluation systems for domain-specific requirements
Implements a submission workflow where model authors can register their code generation models for evaluation through a structured form interface. The system validates model metadata, queues submissions for automated evaluation, and publishes results to the leaderboard with minimal manual intervention. Uses Gradio forms to collect model identifiers and configuration, then orchestrates evaluation jobs asynchronously.
Unique: Uses Gradio form interface for low-friction model submission combined with asynchronous evaluation orchestration, enabling community contributions without requiring direct infrastructure access while maintaining evaluation consistency through automated test harness
vs alternatives: Lower submission friction than manual evaluation request processes, but requires more infrastructure overhead than simple leaderboard aggregation of pre-computed results
Evaluates code generation models across multiple programming languages (Python, Java, JavaScript, Go, C++, etc.) with language-specific test harnesses and execution environments. Each language has dedicated test runners that compile/interpret generated code and validate correctness against expected outputs. The evaluation framework abstracts language-specific details while maintaining consistent pass/fail semantics across languages.
Unique: Implements language-specific test harnesses with dedicated execution environments for each language, enabling fair evaluation across Python, Java, JavaScript, Go, C++ and others while maintaining consistent pass/fail semantics through abstracted evaluation framework
vs alternatives: More comprehensive than single-language benchmarks for assessing generalization, but requires significantly more infrastructure and maintenance than language-agnostic evaluation approaches
Maintains a dynamically updated leaderboard that aggregates benchmark results across all submitted models, computing rankings based on standardized metrics (pass@k scores). The leaderboard updates automatically as new evaluation results are published, sorting models by performance and displaying metadata (model size, architecture, training data, etc.). Uses Gradio table components to render rankings with filtering and sorting capabilities.
Unique: Implements real-time leaderboard updates using Gradio table components with dynamic sorting and filtering, automatically aggregating benchmark results as evaluations complete without requiring manual leaderboard maintenance or batch updates
vs alternatives: Provides immediate visibility into model performance rankings with low operational overhead compared to manually maintained leaderboards, though less flexible than custom dashboards for domain-specific ranking logic
Captures and displays comprehensive metadata for each evaluated model including model size, architecture type, training data sources, license information, and links to model cards and documentation. Metadata is extracted from HuggingFace model repositories and supplemented with submission-provided information. The system maintains provenance information linking models to their source repositories and enabling reproducibility.
Unique: Aggregates metadata from HuggingFace model repositories and submission forms into unified model profiles, maintaining provenance links to source repositories while enabling filtering and search by model characteristics
vs alternatives: Provides centralized metadata access without requiring manual curation, though less comprehensive than specialized model registry systems that track additional runtime and deployment characteristics
Publishes complete evaluation results including test cases, model outputs, and pass/fail status for public inspection, enabling independent verification of benchmark results. Results are stored persistently and linked from leaderboard entries, allowing researchers to audit evaluation methodology and identify potential issues. The system maintains evaluation logs with timestamps and configuration details for reproducibility.
Unique: Publishes complete evaluation artifacts including test cases, model outputs, and execution logs for public inspection, enabling independent verification and reproducibility while maintaining evaluation integrity through standardized test harness
vs alternatives: Provides higher transparency than closed evaluation systems, though creates risk of benchmark overfitting and requires careful management of test case disclosure to maintain benchmark validity
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 bigcode-models-leaderboard at 22/100. bigcode-models-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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