| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Aggregates job listings from 175,000+ company career sites across 54 different ATS platforms (Workday, Greenhouse, Ashby, Lever, Rippling, SuccessFactors, iCIMS, ADP, and others) through a unified MCP interface. The system crawls and normalizes job data from heterogeneous ATS sources into a standardized schema, enabling single-query access to jobs regardless of underlying platform. Implements platform-specific parsing logic to extract job details from each ATS's unique HTML/API structure and reconciles data formats into consistent output fields.
Unique: Unified MCP interface abstracting 54 different ATS platforms into a single query mechanism, with AI-enriched job data and LinkedIn company enrichment — eliminates need to build separate integrations for Workday, Greenhouse, Ashby, Lever, etc. individually
vs alternatives: Broader ATS platform coverage (54 platforms) and AI enrichment layer compared to single-platform APIs; MCP protocol enables tighter LLM agent integration than traditional REST endpoints
Applies AI-driven enrichment to raw job listings scraped from diverse ATS platforms, standardizing unstructured job descriptions into consistent, queryable fields and augmenting data with derived insights. The enrichment pipeline processes job titles, descriptions, and requirements through NLP models to extract structured metadata (required skills, experience level, job category, salary ranges where not explicitly provided) and reconciles formatting inconsistencies across different ATS platforms. Integrates LinkedIn company data enrichment to add organizational context (company size, industry, growth stage) to each job listing.
Unique: Combines ATS aggregation with AI-driven enrichment pipeline that extracts structured fields (skills, experience level, job category) from unstructured descriptions and reconciles formatting across 54 ATS platforms — most ATS aggregators provide raw data without enrichment
vs alternatives: Provides enriched, queryable job data out-of-the-box versus competitors requiring separate NLP pipelines for skill extraction and company data enrichment
Exposes job listing retrieval and querying as MCP tools callable directly by LLM agents and AI assistants, enabling natural language job search and analysis without custom API integration code. Implements MCP tool schema definitions for job queries, filtering, and pagination, allowing Claude, other LLMs, and autonomous agents to invoke job retrieval as part of multi-step reasoning workflows. The MCP transport layer (stdio, SSE, or HTTP) handles serialization and context passing between LLM agents and the job data backend, enabling agents to compose job queries with other tools in a unified execution environment.
Unique: Native MCP server implementation enabling direct LLM agent tool calling for job queries, with standardized MCP schema — eliminates need for custom API wrapper code or function-calling schema definitions in agent frameworks
vs alternatives: Tighter LLM agent integration than REST API endpoints; agents can invoke job queries as native MCP tools without custom function definitions or API client libraries
Implements metered billing model where job retrieval costs $4.00 per 1,000 jobs retrieved, with underlying costs mapped to Apify compute units ($0.13-$0.20 per unit depending on plan). Billing is integrated with Apify platform account, enabling transparent cost tracking and budget management through Apify's usage dashboard. The pricing model incentivizes efficient queries and result filtering, as each job retrieved incurs cost regardless of whether all fields are consumed by the client.
Unique: Transparent per-job pricing ($4.00 per 1,000 jobs) mapped to Apify compute units, enabling cost prediction and budget management through Apify's native billing system — avoids hidden costs or surprise charges
vs alternatives: More transparent and predictable than subscription-based job APIs; pay-as-you-go model suits variable consumption patterns better than fixed monthly tiers
Companion capability provided through the 'Career Site Job Listing Feed' product (4.8★ rating), offering streaming or feed-based access to job updates as an alternative to on-demand query API. The feed model continuously monitors indexed career sites and publishes new job listings, job updates, and job removals as events, enabling subscribers to stay synchronized with job market changes without polling. This architecture suits real-time job board applications and continuous aggregation pipelines that need immediate notification of job changes rather than batch retrieval.
Unique: Streaming feed alternative to on-demand API queries, enabling real-time job market monitoring across 175k+ career sites without polling — complements query API for use cases requiring continuous updates
vs alternatives: Feed-based model reduces polling overhead and provides real-time updates compared to periodic batch queries; better suited for continuously-updated job boards than on-demand API calls
Ecosystem of specialized MCP servers and APIs for individual ATS platforms (Workday Jobs API 5.0★, Greenhouse Jobs API 3.0★, Ashby Jobs API, Lever.co Jobs API, ADP Jobs API) enabling developers to integrate with specific platforms at higher fidelity than the aggregated multi-ATS API. Each platform-specific variant provides native access to platform-specific fields, features, and capabilities without normalization or abstraction, allowing deeper integration with particular ATS systems. Developers can choose between the unified aggregation API for broad coverage or platform-specific APIs for deeper integration with particular systems.
Unique: Ecosystem of platform-specific MCP servers (Workday, Greenhouse, Ashby, Lever, ADP) enabling native integration with particular ATS systems at higher fidelity than aggregated API — developers choose between unified coverage or platform-specific depth
vs alternatives: Platform-specific variants provide native API access and platform-specific fields versus aggregated API's normalized abstraction; enables deeper integrations for teams committed to specific ATS platforms
Companion 'Expired Jobs API' capability that tracks job listings that have been removed or expired from company career sites, enabling job boards and aggregators to maintain accurate, current job listings by detecting and removing stale postings. The system monitors previously-indexed jobs and detects when they are no longer available on career sites, providing removal events or expired job data that allows clients to clean up their job databases. This capability is essential for maintaining data quality in aggregated job boards where jobs may be removed without explicit notification.
Unique: Dedicated expired job tracking API that monitors job removal across 175k+ career sites, enabling automatic stale job detection and removal — most job aggregators lack explicit removal tracking
vs alternatives: Dedicated removal detection versus manual job validation or periodic re-crawling; enables proactive data quality maintenance in aggregated job boards
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 Career Site Jobs at 18/100. Supabase also has a free tier, making it more accessible.
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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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