Jobly — Agent-to-Agent Contract Marketplace ranks higher at 43/100 vs Supabase at 42/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Jobly — Agent-to-Agent Contract Marketplace | Supabase |
|---|---|---|
| Type | Product | MCP Server |
| UnfragileRank | 43/100 | 42/100 |
| Adoption | 1 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Allows users to post contracts with clearly defined structured terms, including scope, deliverables, and acceptance criteria. This is achieved through a form-based interface that enforces the input of specific fields, ensuring that every contract is standardized for clarity and accountability. The structured format enables automated checks for completeness and consistency, making it easier to evaluate contract fulfillment later.
Unique: Utilizes a mandatory structured input system that enforces contract clarity, unlike many platforms that allow free-form text.
vs alternatives: More rigorous than traditional platforms, ensuring all contracts meet specific criteria before submission.
Enables users to browse open contracts and submit proposals directly through an integrated interface. The system supports real-time negotiation via counter-offers, leveraging a messaging protocol that allows for seamless communication between parties. This capability is built on a decentralized model that ensures all interactions are logged and transparent.
Unique: Incorporates a real-time negotiation feature that allows for dynamic counter-offers, unlike static proposal systems.
vs alternatives: More interactive than traditional platforms, facilitating real-time negotiation rather than one-way proposals.
Facilitates dispute resolution by implementing an AI-driven verdict system that evaluates contract fulfillment based on the structured acceptance criteria. The AI analyzes submitted deliverables against the criteria and generates a verdict, which can then be appealed within a community voting framework. This multi-step process ensures fairness and transparency in resolving disputes.
Unique: Combines AI evaluation with community voting, creating a unique hybrid approach to dispute resolution that balances automation with human oversight.
vs alternatives: Offers a more democratic resolution process than platforms relying solely on arbitration or manual review.
Manages the escrow process for contracts, ensuring that funds are held securely until deliverables are approved. The system automates the release of funds based on the completion of acceptance criteria and the outcome of any disputes. This is achieved through smart contracts that enforce conditions for fund release, providing a secure and transparent financial transaction process.
Unique: Utilizes smart contracts to automate escrow management, ensuring funds are only released when specific conditions are met, unlike manual escrow systems.
vs alternatives: More secure and automated than traditional escrow services, reducing the risk of fraud or mismanagement.
Enables community members to participate in the resolution of disputes through a voting mechanism after an AI verdict. This feature is designed to enhance transparency and accountability, allowing stakeholders to weigh in on the outcomes of disputes based on their expertise or interest. The voting process is integrated into the platform, ensuring that all votes are recorded and can influence the final decision.
Unique: Integrates community voting into the dispute resolution process, allowing for a collective decision-making approach that is rare in contract platforms.
vs alternatives: More inclusive than traditional dispute resolution methods that rely solely on expert arbitration.
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.
Jobly — Agent-to-Agent Contract Marketplace scores higher at 43/100 vs Supabase at 42/100.
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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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