| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Analyzes project dependency graphs and automatically generates code migrations when upgrading library versions. Uses abstract syntax tree (AST) parsing to identify breaking API changes, deprecated function calls, and signature modifications across multiple languages, then applies targeted refactoring rules to update call sites, imports, and configuration files without manual intervention.
Unique: Combines AST-based code analysis with curated migration rule libraries to perform language-aware refactoring at scale, rather than regex-based find-and-replace or manual changelog interpretation
vs alternatives: More precise than generic code search tools because it understands semantic code structure; more scalable than manual migration guides because it automates application across entire codebases
Orchestrates complex, multi-step framework upgrades (e.g., React 17→18, Next.js 12→13, Django 3→4) by coordinating changes across interdependent files, configuration files, and transitive dependencies. Manages upgrade sequencing, handles cascading changes where one file's update triggers requirements in others, and validates consistency across the entire upgrade path.
Unique: Handles cascading, interdependent changes across multiple file types and configuration formats in a single coordinated operation, rather than treating each file independently
vs alternatives: More reliable than following upgrade guides manually because it ensures all interdependent changes are applied together; faster than incremental manual upgrades because it parallelizes independent changes
Applies language-specific transformation rules to modernize code patterns, enforce style standards, or adapt to new language features. Uses pattern matching and code rewriting engines to identify outdated idioms (e.g., var→const, callback→async-await, string concatenation→template literals) and automatically rewrite them while preserving semantics and comments.
Unique: Uses declarative pattern-matching rules that can express complex syntactic transformations while preserving code semantics, rather than simple regex substitution or manual refactoring
vs alternatives: More precise than linters because it can automatically fix violations rather than just reporting them; more flexible than language-specific tools because rules can be customized for project-specific patterns
Automatically migrates configuration files (JSON, YAML, TOML, etc.) when their schemas change due to library or framework updates. Handles nested structure transformations, renames deprecated keys, applies default values for new required fields, and validates the output against the new schema specification.
Unique: Treats configuration migration as a structured data transformation problem with schema validation, rather than treating config files as unstructured text
vs alternatives: More reliable than manual config updates because it validates against the new schema; more maintainable than custom migration scripts because rules are declarative and reusable
Scans an entire codebase to identify all usages of deprecated APIs, breaking changes, and compatibility issues before executing migrations. Generates detailed impact reports showing which files are affected, how many changes are needed, and potential risks or manual review requirements, enabling informed decision-making about upgrade feasibility.
Unique: Provides pre-migration analysis and impact quantification before any changes are applied, enabling informed decision-making rather than discovering issues during or after migration
vs alternatives: More comprehensive than running a linter because it understands semantic breaking changes, not just style violations; more actionable than reading changelogs because it shows exactly which files in your codebase are affected
Automatically generates or adapts test cases to validate that migrations preserve application behavior. Runs tests before and after migration to detect regressions, generates new tests for migrated code patterns, and provides detailed reports on test coverage of migrated code to ensure confidence in the changes.
Unique: Integrates test execution and validation into the migration workflow itself, comparing behavior before and after to detect regressions automatically
vs alternatives: More thorough than manual testing because it runs comprehensive test suites automatically; more reliable than code review alone because it provides objective evidence of behavioral preservation
Enables phased migrations by applying changes to selected files or modules first, validating them, and then progressively rolling out to the rest of the codebase. Maintains rollback capability at each stage, allowing teams to revert to previous versions if issues are discovered, and tracks migration state across multiple sessions.
Unique: Provides state management and rollback capabilities for migrations, treating them as deployable changes rather than one-time transformations
vs alternatives: Safer than full-codebase migrations because it enables validation and rollback at each stage; more flexible than all-or-nothing approaches because teams can adapt to discovered issues
Handles migrations in polyglot codebases where multiple languages are used (e.g., TypeScript frontend, Python backend, Go services). Understands cross-language dependencies and API contracts, ensuring that when a backend API changes, corresponding frontend code is updated to match, and vice versa.
Unique: Understands and coordinates changes across language boundaries, treating polyglot codebases as a unified system rather than independent language-specific projects
vs alternatives: More comprehensive than language-specific migration tools because it ensures consistency across the entire system; more reliable than manual coordination because it enforces API contract consistency automatically
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 Second at 19/100. Supabase also has a free tier, making it more accessible.
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