Supabase ranks higher at 42/100 vs Twenty CRM Integration Server at 30/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Twenty CRM Integration Server | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 30/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to interact with their CRM data using natural language commands, leveraging NLP techniques to parse and understand user intents. It employs a type-safe command parser that validates inputs against the CRM schema, ensuring that actions like creating contacts or updating opportunities are executed accurately. This approach minimizes errors and enhances user experience by providing immediate feedback on command validity.
Unique: Utilizes a type-safe command parser that integrates directly with the Twenty CRM schema, ensuring high accuracy in command execution.
vs alternatives: More accurate than traditional keyword-based systems due to its type-safe validation against the CRM schema.
This capability automates CRM workflows by allowing users to define sequences of actions triggered by specific events or commands. It uses a rule-based engine to interpret user-defined workflows and execute them in response to CRM events, such as new lead creation or task completion. This orchestration is designed to reduce manual effort and improve productivity by automating repetitive tasks.
Unique: Features a rule-based engine that allows for complex, event-driven workflows tailored to specific CRM events.
vs alternatives: More flexible than static workflow tools, enabling dynamic responses to CRM events.
This capability provides a type-safe interface for managing contacts within the CRM, ensuring that all contact data adheres to predefined schemas. It employs validation checks at the point of data entry, which prevents invalid data from being saved and maintains data integrity. This is achieved through a combination of schema validation and user feedback mechanisms.
Unique: Incorporates a robust validation layer that checks contact data against a defined schema before saving.
vs alternatives: More reliable than traditional systems that lack real-time validation, reducing data entry errors.
This capability allows users to track and manage sales opportunities within the CRM, utilizing a structured interface that aligns with the sales process. It integrates with the CRM's opportunity data model, enabling users to update statuses, add notes, and assign tasks directly through the interface. This structured approach ensures that all opportunity-related actions are consistent and traceable.
Unique: Directly integrates with the CRM's opportunity data model, ensuring all updates are consistent with the sales process.
vs alternatives: More streamlined than generic CRM tools, providing a focused interface for opportunity management.
This capability enables users to log activities related to contacts and opportunities within the CRM, automatically generating reports based on user-defined criteria. It employs a logging framework that captures activity data in real-time and allows users to generate reports through simple queries. This feature enhances visibility into user interactions and helps identify trends over time.
Unique: Features real-time logging capabilities that allow for immediate reporting and trend analysis based on user activities.
vs alternatives: More responsive than traditional logging systems, providing instant feedback and reporting 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 Twenty CRM Integration Server 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