Supabase ranks higher at 42/100 vs LinkedIn Profile and Job Scraper at 28/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | LinkedIn Profile and Job Scraper | Supabase |
|---|---|---|
| Type | MCP Server | 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 utilizes a web scraping engine to extract detailed information from LinkedIn profiles while managing user sessions securely. It employs a session management pattern to handle authentication tokens and cookies, ensuring that data is scraped in compliance with LinkedIn's usage policies. The architecture allows for efficient data retrieval and minimizes the risk of being blocked by LinkedIn's anti-scraping measures.
Unique: Incorporates advanced session management to maintain user authentication and avoid detection, unlike simpler scrapers that may not handle sessions effectively.
vs alternatives: More resilient against LinkedIn's anti-scraping measures compared to basic scrapers that lack session handling.
This capability enables the extraction of job postings from LinkedIn by parsing the job listing pages and capturing relevant details such as job title, company, location, and description. It uses a combination of HTML parsing techniques and XPath queries to accurately locate and extract the required data fields. The implementation is designed to adapt to changes in LinkedIn's page structure, ensuring ongoing functionality.
Unique: Utilizes adaptive HTML parsing techniques that can quickly adjust to LinkedIn's UI changes, unlike static parsers that may break easily.
vs alternatives: More reliable in extracting job postings compared to alternatives that struggle with frequent UI updates.
This capability focuses on extracting detailed information about companies from LinkedIn, including company size, industry, and employee count. It employs a structured approach to navigate LinkedIn's company pages and uses data extraction libraries to pull relevant information efficiently. The design allows for batch processing of multiple company profiles, optimizing the data retrieval process.
Unique: Features batch processing capabilities that allow simultaneous extraction of multiple company profiles, enhancing efficiency over single-threaded scrapers.
vs alternatives: More efficient for bulk company data extraction compared to alternatives that handle one profile at a time.
This capability ensures secure handling of LinkedIn credentials by encrypting sensitive information and managing sessions through secure storage solutions. It uses best practices in credential management to prevent unauthorized access and ensures that scraping operations comply with LinkedIn's terms of service. The architecture includes secure token storage and retrieval mechanisms to maintain user privacy.
Unique: Employs advanced encryption techniques for credential storage, ensuring a higher level of security than typical plaintext storage methods.
vs alternatives: Offers superior security for credential management compared to simpler implementations that may expose sensitive data.
This capability allows for session-based retrieval of data from LinkedIn, ensuring that each scraping operation maintains the context of the user session. It uses a stateful session management approach to keep track of user interactions and data requests, which helps in avoiding detection and blocking by LinkedIn. The architecture is designed to handle multiple concurrent sessions efficiently.
Unique: Utilizes a stateful session management system that allows for concurrent scraping of multiple accounts, unlike simpler implementations that may struggle with session handling.
vs alternatives: More effective at managing multiple sessions simultaneously compared to basic scrapers that can only handle one session at a time.
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 LinkedIn Profile and Job Scraper at 28/100.
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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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