| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures the complete schema definitions of MCP (Model Context Protocol) tools by introspecting tool registries and serializing them into a canonical JSON format. This enables version control and diffing of tool contracts by converting runtime tool definitions into persistent, comparable schema artifacts that preserve type information, parameter constraints, and documentation.
Unique: Implements MCP-specific schema introspection that understands the Model Context Protocol's tool definition structure, capturing not just function signatures but the full MCP schema semantics including resource hints and sampling directives
vs alternatives: Purpose-built for MCP tool contracts rather than generic OpenAPI/JSON Schema tools, enabling capture of MCP-specific metadata that generic schema tools would lose
Compares two captured MCP tool schema snapshots and produces a structured diff report identifying additions, removals, modifications, and breaking changes at the parameter, type, and constraint levels. Uses a line-aware diffing algorithm that maps schema changes to human-readable change descriptions, enabling developers to understand exactly what contract changes occurred between versions.
Unique: Implements MCP-aware diff logic that understands tool schema semantics beyond string comparison, classifying changes as breaking/non-breaking based on MCP contract rules and parameter compatibility
vs alternatives: More intelligent than generic JSON diff tools because it understands MCP schema semantics and can classify changes as breaking or safe based on tool contract compatibility rules
Provides command-line interface for integrating schema capture and diff operations into development workflows, shell scripts, and CI/CD pipelines. Supports piping, file I/O, and exit code signaling for integration with standard Unix tooling and automation frameworks, enabling schema validation as a build step or pre-deployment check.
Unique: Designed as a Unix-philosophy CLI tool with proper exit codes and piping support, enabling seamless integration into shell scripts and CI/CD systems without requiring Node.js knowledge
vs alternatives: More accessible than programmatic APIs for shell-based workflows and CI/CD systems, with standard exit code conventions and text output suitable for log parsing
Manages persistent storage of MCP tool schema snapshots as versioned artifacts, enabling historical tracking and comparison across multiple schema states. Stores snapshots in a format suitable for version control (git-friendly JSON), allowing teams to maintain a complete audit trail of tool contract evolution and revert to previous schema states if needed.
Unique: Generates git-friendly JSON snapshots that minimize diff noise through consistent formatting and key ordering, making schema changes visible in git diffs without spurious whitespace changes
vs alternatives: Better suited for git-based workflows than binary schema formats because JSON diffs are human-readable and can be reviewed in pull requests
Validates captured MCP tool schemas against the Model Context Protocol specification, ensuring that tool definitions conform to MCP requirements for parameter types, naming conventions, and schema structure. Performs structural validation that catches schema errors before they propagate to clients, providing detailed error messages that guide developers toward compliant schemas.
Unique: Implements MCP specification validation that understands the protocol's specific requirements for tool schemas, including resource hints, sampling directives, and parameter constraints that generic JSON Schema validators would miss
vs alternatives: More comprehensive than generic JSON Schema validation because it enforces MCP-specific rules and conventions that ensure interoperability with MCP clients
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 @mcp-contracts/cli at 21/100. @mcp-contracts/cli leads on ecosystem, while Supabase is stronger on quality.
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