| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Captures and persists the current state of MCP tool schemas at a point in time, creating a baseline snapshot that can be compared against future versions. Uses a serialization approach to store schema definitions in a queryable format, enabling historical tracking of tool interface evolution without requiring external databases or version control systems.
Unique: Provides MCP-specific schema snapshotting that understands the Model Context Protocol's tool definition structure, including parameter schemas, resource definitions, and capability declarations, rather than generic JSON diffing
vs alternatives: Specialized for MCP contracts whereas generic schema versioning tools (like JSON Schema validators) lack MCP-specific semantics and cannot classify breaking vs non-breaking changes in the MCP context
Compares two MCP tool schema snapshots and computes a detailed diff that identifies additions, removals, modifications, and structural changes at multiple levels (tool-level, parameter-level, type-level). Uses a recursive comparison algorithm that traverses schema hierarchies and produces a structured diff representation that preserves context about what changed and where.
Unique: Implements MCP-aware structural diffing that understands tool definitions, input/output schemas, and resource patterns, producing diffs that classify changes within MCP semantics rather than generic JSON property changes
vs alternatives: More precise than generic diff tools (like deep-diff or json-diff) because it understands MCP schema structure and can identify semantically meaningful changes like parameter reordering vs parameter removal
Automatically classifies schema changes as breaking or non-breaking based on MCP compatibility rules and semantic analysis. Implements a rule engine that evaluates changes against known breaking patterns (e.g., removing required parameters, changing parameter types, removing tools) and assigns risk classifications that help teams assess deployment impact without manual review.
Unique: Encodes MCP-specific breaking change rules that understand tool invocation contracts, parameter binding semantics, and resource availability guarantees, rather than generic schema compatibility rules
vs alternatives: More accurate than generic schema validators because it understands MCP's specific compatibility model, whereas tools like JSON Schema validators apply generic schema rules that don't capture MCP-specific breaking patterns
Validates MCP tool schemas against the Model Context Protocol specification and contract requirements, ensuring schemas conform to MCP's defined structure, naming conventions, and capability declarations. Uses a validation rule set that checks for required fields, type correctness, and semantic validity within the MCP context, producing detailed validation reports with specific error locations.
Unique: Implements validation rules specific to MCP's schema contract model, including tool capability declarations, resource patterns, and parameter binding semantics, rather than generic JSON schema validation
vs alternatives: More comprehensive than generic JSON Schema validators because it enforces MCP-specific requirements like tool naming conventions, capability declarations, and resource availability patterns that generic validators cannot express
Generates a compatibility matrix that shows which versions of MCP tools are compatible with which client versions, based on schema evolution history and breaking change analysis. Computes transitive compatibility relationships across multiple schema versions, enabling teams to understand upgrade paths and deprecation timelines without manual analysis.
Unique: Computes MCP-specific compatibility matrices that understand tool invocation contracts and parameter binding semantics, producing compatibility graphs that reflect actual MCP client-server compatibility rather than generic version compatibility
vs alternatives: More useful than generic semantic versioning tools because it produces actionable compatibility matrices specific to MCP's tool invocation model, whereas generic tools only track version numbers without semantic compatibility analysis
Analyzes schema changes to identify downstream impacts on MCP clients, including affected tool invocations, parameter binding changes, and resource availability modifications. Produces detailed impact reports that quantify the scope of change (number of affected tools, parameters, resources) and provide recommendations for client-side adaptations, enabling teams to assess migration effort.
Unique: Provides MCP-specific impact analysis that understands tool invocation patterns and parameter binding semantics, quantifying impacts in terms of affected tool calls and client adaptations rather than generic schema change counts
vs alternatives: More actionable than generic change impact tools because it produces MCP-specific impact metrics and migration recommendations, whereas generic tools only report structural changes without understanding MCP client-server interaction patterns
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/core at 23/100. @mcp-contracts/core leads on ecosystem, while Supabase is stronger on quality.
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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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