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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Routes API requests to multiple LLM providers (OpenAI, Anthropic, Google, Meta, Mistral, etc.) through a single standardized endpoint, abstracting provider-specific API schemas and authentication. Implements a request normalization layer that translates unified OpenRouter API calls into provider-native formats, handling differences in parameter naming, token counting, and response structures across 100+ models.
Unique: Implements a request normalization layer that translates unified API calls into provider-native schemas while maintaining feature parity across 100+ models, rather than forcing providers into a lowest-common-denominator interface
vs alternatives: Broader provider coverage (100+ models) and automatic request translation than LiteLLM, with simpler setup than building custom provider adapters
Enables function calling across providers with different native function-calling implementations (OpenAI's tool_choice, Anthropic's tool_use, etc.) by accepting a unified JSON schema and translating it to each provider's format. Handles response parsing to extract function calls regardless of provider-specific response structure, normalizing tool_calls into a consistent format.
Unique: Translates unified JSON schemas into provider-specific function calling formats (OpenAI tool_use, Anthropic tool_use, etc.) and normalizes responses back to a consistent structure, enabling true provider interchangeability for agentic workflows
vs alternatives: Handles function calling translation across more providers than alternatives, with automatic fallback to text extraction for models without native support
Exposes real-time pricing data (input/output token costs) for all available models, enabling developers to programmatically select models based on cost-performance tradeoffs. Provides model metadata including context window size, training data cutoff, and capabilities, allowing cost-aware routing logic without manual price lookups.
Unique: Aggregates and exposes standardized pricing and capability metadata across 100+ models from different providers in a single API, enabling programmatic cost-performance optimization without manual research
vs alternatives: More comprehensive pricing transparency than individual provider APIs, with structured metadata enabling automated cost-aware routing
Supports Server-Sent Events (SSE) streaming for real-time token generation across all providers, normalizing streaming response formats (OpenAI's delta objects, Anthropic's content_block_delta, etc.) into a unified stream format. Handles stream interruption, error propagation, and graceful fallback to non-streaming responses.
Unique: Normalizes streaming response formats across providers with different SSE implementations, translating provider-specific delta structures into a unified format while maintaining real-time performance
vs alternatives: Simpler streaming integration than managing provider-specific SSE formats directly, with unified error handling across all providers
Automatically logs all API requests and responses with metadata including provider, model, tokens used, latency, and cost. Provides dashboard and API access to request history, enabling usage analytics, cost tracking, and performance monitoring across all providers without application-level instrumentation.
Unique: Provides automatic, zero-configuration logging and analytics across all providers with unified cost attribution and performance metrics, without requiring application-level instrumentation
vs alternatives: Unified analytics across 100+ models from different providers, vs. managing separate logging for each provider's API
Provides accurate token counting for each model using model-specific tokenizers (not generic approximations), accounting for differences in how providers count tokens (e.g., OpenAI vs. Anthropic token boundaries). Exposes context window limits and handles context overflow warnings before requests are sent.
Unique: Uses model-specific tokenizers rather than generic approximations, accounting for provider-specific token counting differences (OpenAI vs. Anthropic vs. others) to provide accurate pre-request token estimates
vs alternatives: More accurate token counting than generic approximations, with provider-specific precision vs. manual estimation or post-request token usage
Implements automatic failover to alternative providers/models when a request fails, with configurable retry policies (exponential backoff, max retries, timeout handling). Transparently switches providers based on availability, error type, and user-defined fallback chains without requiring application-level retry logic.
Unique: Implements transparent provider failover with configurable retry chains, automatically switching providers based on error type and availability without requiring application-level retry logic
vs alternatives: Simpler failover configuration than building custom retry logic per provider, with automatic provider switching vs. manual fallback handling
Exposes structured metadata about model capabilities (vision support, function calling, long context, etc.) enabling programmatic filtering and discovery. Allows querying models by capability (e.g., 'find all models with vision support under $0.01 per 1K tokens') without manual research or hardcoded model lists.
Unique: Provides structured, queryable capability metadata across 100+ models from different providers, enabling programmatic model discovery and filtering without manual research or hardcoded lists
vs alternatives: Unified capability discovery across all providers vs. checking individual provider documentation, with structured filtering vs. manual model selection
+2 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 OpenRouter at 20/100. OpenRouter leads on quality, while Supabase is stronger on ecosystem. Supabase also has a free tier, making it more accessible.
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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