Supabase ranks higher at 42/100 vs deberta-v3-xsmall-zeroshot-v1.1-all-33 at 38/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | deberta-v3-xsmall-zeroshot-v1.1-all-33 | Supabase |
|---|---|---|
| Type | Model | MCP Server |
| UnfragileRank | 38/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Classifies text into arbitrary user-defined categories without requiring labeled training data, using DeBERTa-v3's contrastive learning architecture to map input text and candidate labels into a shared embedding space, then computing similarity scores to determine the most probable class. The model was fine-tuned on 33 diverse NLI datasets to generalize across domain-specific classification tasks, enabling dynamic category definition at inference time without retraining.
Unique: Trained on 33 diverse NLI datasets (vs typical 1-3 dataset fine-tuning) to maximize generalization across unseen classification domains; uses DeBERTa-v3's disentangled attention mechanism which separates content and position embeddings, improving semantic understanding for zero-shot transfer compared to BERT-based alternatives
vs alternatives: Smaller and faster than zero-shot alternatives (BART, T5) while maintaining competitive accuracy through NLI pre-training; outperforms GPT-3.5 zero-shot on structured classification tasks with 100x lower latency and no API costs
Provides pre-quantized weights and ONNX Runtime-compatible serialization to enable sub-100ms inference on CPU and edge devices. The xsmall variant (22M parameters) is quantized to int8 precision, reducing model size from ~90MB to ~45MB while maintaining classification accuracy within 1-2% of full precision. ONNX export enables hardware-accelerated inference across CPU, GPU, and specialized accelerators (TPU, NPU) without PyTorch dependency.
Unique: Pre-quantized int8 weights provided alongside full-precision checkpoint, eliminating need for users to perform quantization; ONNX export includes optimized graph transformations for DeBERTa's disentangled attention, preserving architectural benefits during inference
vs alternatives: Faster CPU inference than PyTorch baseline (3-5x speedup via ONNX Runtime) and smaller model size than unquantized alternatives, enabling deployment to resource-constrained environments where larger zero-shot models (BART, T5) are infeasible
Scores each candidate label independently against input text, enabling multi-label classification where a single text can be assigned multiple categories simultaneously. Unlike single-label classification, the model computes similarity scores for each label without forcing a winner-take-all decision, allowing downstream applications to set custom thresholds per label or use all scores for ranking-based decisions.
Unique: Leverages NLI training to score labels independently without explicit multi-label fine-tuning; DeBERTa's attention mechanism allows the model to evaluate each label's relevance to the input text in isolation, avoiding label interference that occurs in models trained with multi-label loss functions
vs alternatives: More flexible than single-label classifiers and avoids the computational overhead of true multi-label models (which require exponential label combinations); enables threshold-based filtering that single-label models cannot provide
While trained exclusively on English NLI data, the model can perform zero-shot classification on non-English text through cross-lingual transfer, leveraging multilingual token embeddings in the DeBERTa-v3 tokenizer. When given non-English input text and English candidate labels, the model maps both to a shared semantic space, enabling classification in languages not explicitly seen during training. Performance degrades gracefully with language distance from English.
Unique: Achieves cross-lingual transfer without explicit multilingual training through DeBERTa-v3's shared token embeddings; NLI training on English data generalizes to non-English input because the entailment task (does premise entail hypothesis?) is language-agnostic at the semantic level
vs alternatives: Simpler and faster than maintaining separate language-specific models; outperforms naive machine translation + English classification on latency-sensitive systems, though accuracy is lower than true multilingual models (mBERT, XLM-R)
Processes multiple text samples in a single batch while allowing each sample to have a different set of candidate labels, without requiring padding or masking of label sets. The model computes classification scores for each (text, label) pair independently, enabling efficient vectorized inference where batch size and label set heterogeneity do not impact computational complexity. Useful for scenarios where label sets vary by sample (e.g., product categorization where different products have different valid categories).
Unique: Supports heterogeneous label sets per sample without padding or masking, leveraging DeBERTa's efficient attention mechanism to compute independent (text, label) scores in parallel; enables true dynamic classification where label vocabulary is not fixed at model initialization
vs alternatives: More flexible than fixed-vocabulary classifiers; avoids padding overhead of models that require uniform label set sizes, reducing memory usage and latency for variable-label-set scenarios
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 deberta-v3-xsmall-zeroshot-v1.1-all-33 at 38/100.
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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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