Supabase ranks higher at 42/100 vs Clockify Time Tracking Integration Server at 28/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Clockify Time Tracking Integration Server | Supabase |
|---|---|---|
| Type | Product | MCP Server |
| UnfragileRank | 28/100 | 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 for the management of workspaces through a Model-Context Protocol (MCP) interface, enabling seamless communication with Clockify's API. It utilizes a RESTful architecture to create, update, and delete workspaces while maintaining state consistency across multiple clients. The integration is designed to handle concurrent requests efficiently, ensuring that workspace data remains synchronized in real-time.
Unique: Utilizes a stateful MCP design that allows for real-time updates and synchronization across multiple clients, unlike traditional REST APIs.
vs alternatives: More efficient in handling concurrent workspace updates compared to standard REST API implementations.
This capability automates user management tasks such as adding, updating, and removing users from Clockify workspaces. It leverages the MCP architecture to ensure that all user actions are processed in a secure and efficient manner, with built-in validation and error handling to prevent data inconsistencies. The integration allows for bulk user operations, significantly reducing the time required for user management.
Unique: Employs a robust error handling mechanism that ensures data integrity during bulk user operations, setting it apart from simpler implementations.
vs alternatives: Faster and more reliable than manual user management interfaces provided by Clockify.
This capability synchronizes projects and tasks between Clockify and external systems using the MCP framework. It allows for real-time updates and retrieval of project details, ensuring that any changes made in one system are reflected in the other. The architecture supports webhook notifications, enabling proactive updates without polling, which reduces latency and improves performance.
Unique: Incorporates webhook support for real-time updates, which is not commonly found in other integration solutions.
vs alternatives: More responsive than traditional polling methods used by other integration tools.
This capability provides a comprehensive API for managing time entries in Clockify, allowing users to create, update, and delete time entries programmatically. It utilizes a structured data approach to ensure that all time entries are validated against Clockify's business rules before submission. The API also supports batch processing of time entries, optimizing performance for high-volume use cases.
Unique: Supports batch processing of time entries with validation, which enhances efficiency compared to single-entry APIs.
vs alternatives: More efficient for high-volume time tracking than standard single-entry APIs.
This capability enables the generation and retrieval of reports from Clockify's data through a structured API. It allows users to specify report parameters and formats, such as CSV or PDF, and retrieves the generated reports efficiently. The integration supports asynchronous processing, allowing users to request reports without blocking their applications, improving overall user experience.
Unique: Offers asynchronous report generation, allowing for non-blocking operations, which is not typical in many reporting APIs.
vs alternatives: More user-friendly than synchronous report generation methods that block application flow.
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 Clockify Time Tracking Integration Server at 28/100.
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