| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Analyzes incoming user requests before they reach the LLM to classify intent type, extract semantic meaning, and route to appropriate handlers or memory contexts. Uses semantic classification patterns to determine whether a request is a query, command, context-setting, or multi-step task, enabling downstream systems to prepare relevant data and behavioral context before processing.
Unique: Implements pre-inference classification as an MCP middleware layer that intercepts requests before they reach the LLM, enabling context injection and routing decisions at the protocol level rather than within prompt engineering or post-processing
vs alternatives: Avoids forcing the LLM to perform its own routing logic, reducing token consumption and latency compared to in-prompt routing or post-hoc classification
Retrieves and injects relevant memory, knowledge, and behavioral context into the LLM's input based on semantic similarity to the current request. Uses vector embeddings or knowledge graph traversal to identify related past interactions, domain knowledge, and user preferences, then prepends or augments the prompt with this context to improve response quality and consistency without explicit retrieval calls from the LLM.
Unique: Operates as an MCP middleware that performs memory retrieval and injection at the protocol level before the LLM sees the request, enabling transparent context augmentation across heterogeneous LLM providers without requiring provider-specific APIs or prompt engineering
vs alternatives: Decouples memory management from LLM-specific context window strategies, allowing the same memory system to work across Claude, ChatGPT, Gemini, and other MCP clients without reimplementation
Detects and deduplicates semantically similar requests using embedding-based matching, and caches responses to avoid redundant LLM calls. Identifies requests that are semantically equivalent despite different wording, retrieves cached responses for duplicates, and updates cache based on response quality and staleness. Reduces token consumption and latency for repeated or similar queries without requiring exact string matching.
Unique: Implements semantic deduplication and caching at the MCP middleware level using embedding-based similarity matching, enabling cache hits for semantically equivalent requests without exact string matching or application-level deduplication logic
vs alternatives: Detects semantic duplicates across different phrasings and wordings, reducing token waste compared to exact-match caching or no deduplication; operates transparently across all LLM providers
Logs all requests, responses, and decisions made by the middleware for audit, compliance, and debugging purposes. Records request metadata, selected context, routing decisions, cost information, and response data with timestamps and user attribution. Enables compliance with regulatory requirements (HIPAA, GDPR, SOC 2) and provides visibility into system behavior for debugging and optimization.
Unique: Implements comprehensive audit logging at the MCP middleware layer, capturing all requests, responses, and middleware decisions in a single audit trail, enabling compliance and debugging without requiring application-level logging or provider-specific audit APIs
vs alternatives: Provides unified audit logging across all LLM providers and middleware components, compared to fragmented logging across multiple systems or provider-specific audit trails
Maintains consistent session state, conversation history, and user context across multiple LLM providers (Claude, ChatGPT, Gemini, Cursor, Codex) by storing and retrieving session metadata through a unified MCP interface. Tracks conversation turns, user preferences, and behavioral state independently of the underlying LLM provider, enabling seamless switching between models or multi-model orchestration without losing context.
Unique: Implements session continuity at the MCP protocol layer, abstracting away provider-specific session APIs and enabling a single session store to serve Claude, ChatGPT, Gemini, and other MCP clients simultaneously without provider-specific adapters
vs alternatives: Eliminates the need to maintain separate session stores for each LLM provider; provides unified session semantics across heterogeneous clients compared to provider-native session management
Validates and enforces data quality constraints on requests and responses before they reach the LLM or are returned to the user. Applies schema validation, type checking, format verification, and domain-specific rules to ensure data integrity and consistency. Rejects or transforms invalid data according to configurable policies, preventing malformed inputs from reaching the LLM and ensuring outputs meet quality standards.
Unique: Implements validation as an MCP middleware layer that operates on all requests and responses regardless of LLM provider, enabling consistent data quality enforcement across Claude, ChatGPT, Gemini, and other clients without duplicating validation logic
vs alternatives: Centralizes data quality rules at the protocol level rather than embedding them in prompts or post-processing, reducing token waste and enabling reuse across multiple LLM providers and applications
Injects dynamic behavioral instructions, system prompts, and role-based context into the LLM's input based on the current request, user profile, and session state. Selects and composes appropriate behavioral guidelines, tone, expertise level, and constraints from a configurable library, enabling the same LLM to adapt its behavior across different use cases without explicit user prompts or model fine-tuning.
Unique: Dynamically selects and injects behavioral context at the MCP middleware level based on semantic analysis of the request and user profile, enabling adaptive behavior without explicit user prompting or model fine-tuning
vs alternatives: Separates behavioral customization from prompt engineering, allowing non-technical users to configure LLM behavior through role definitions and context rules rather than manual prompt crafting
Performs semantic search across multiple knowledge domains (documents, past conversations, knowledge graphs, external APIs) to find relevant information for the current request. Uses embedding-based similarity matching and optional relevance ranking to surface the most contextually appropriate results, enabling the LLM to access domain-specific knowledge without explicit user queries or keyword matching.
Unique: Integrates semantic search as an MCP middleware capability that operates transparently across multiple knowledge domains and LLM providers, enabling unified search semantics without provider-specific search APIs or prompt engineering
vs alternatives: Decouples search from LLM inference, enabling faster search iteration and relevance tuning compared to in-prompt search or post-hoc retrieval; supports multi-domain search with a single interface
+4 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 @gramatr/mcp at 30/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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