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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables Claude and other MCP clients to introspect live Convex deployments by exposing app schema, data models, and configuration through the Model Context Protocol. Uses MCP's resource and tool abstractions to surface Convex-specific metadata (tables, functions, auth config) as queryable resources, allowing AI agents to understand app structure without manual documentation or API exploration.
Unique: Bridges Convex's backend-as-a-service platform with MCP protocol, exposing live deployment metadata as queryable resources that AI agents can reason about without custom integrations. Uses Convex's native API to surface real-time schema and function definitions through MCP's standardized resource interface.
vs alternatives: Tighter integration than generic REST API explorers because it understands Convex's data model semantics (documents, mutations, queries) and exposes them as first-class MCP resources rather than generic HTTP endpoints.
Exposes Convex query and mutation functions as callable MCP tools, allowing Claude and other AI agents to execute read and write operations against a live Convex deployment. Implements tool schema mapping where each Convex function becomes an MCP tool with parameter validation, return type coercion, and error handling that translates between Convex's TypeScript function signatures and MCP's JSON-RPC tool calling protocol.
Unique: Dynamically maps Convex's TypeScript function signatures to MCP tool schemas at runtime, enabling type-safe function calling without manual tool definition. Handles Convex-specific patterns like document IDs, references, and validation errors transparently.
vs alternatives: More ergonomic than building custom REST APIs because it automatically exposes Convex functions as tools without boilerplate; tighter type safety than generic HTTP tool calling because it understands Convex's type system.
Maintains a live, queryable context of a Convex deployment's state (schema, functions, data samples, auth rules) that AI agents can reference during reasoning and code generation. Implements context caching and incremental updates so agents can reason about app structure without re-fetching full introspection data on every interaction, reducing latency and token usage in multi-turn conversations.
Unique: Implements MCP-native context management where deployment metadata is cached as queryable resources, allowing agents to reference app structure without repeated introspection calls. Leverages MCP's resource subscription model for incremental updates.
vs alternatives: More efficient than RAG-based approaches because it uses live deployment data rather than stale documentation; more responsive than polling-based context refresh because it can leverage MCP's event-driven resource updates.
Generates type-safe Convex code (queries, mutations, components) by analyzing live deployment schema and function signatures. Uses the introspected schema as context for Claude's code generation, ensuring generated code matches actual table structures, field types, and function parameters without manual type definitions or boilerplate.
Unique: Uses live Convex schema introspection to ground code generation, ensuring generated code is type-correct and schema-compliant without manual type definitions. Integrates schema context directly into Claude's prompt for generation.
vs alternatives: More accurate than generic code generation because it understands Convex's specific patterns (documents, mutations, queries); more maintainable than hand-written boilerplate because it stays in sync with schema changes.
Provides Claude and AI agents with diagnostic information about a live Convex deployment (function execution logs, error traces, performance metrics) through MCP resources. Enables agents to analyze deployment issues, suggest fixes, and explain error patterns by correlating logs with schema and function definitions.
Unique: Exposes Convex deployment diagnostics as MCP resources that agents can query and correlate with schema/function definitions, enabling context-aware debugging. Bridges observability data with code understanding.
vs alternatives: More actionable than raw log access because it contextualizes logs with schema and function information; more efficient than manual debugging because agents can identify patterns across multiple errors.
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 Convex at 17/100. 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