| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes structured job search queries against the Adzuna job board API through MCP protocol bindings, translating natural language or structured parameters (location, keyword, salary range, job type) into HTTP requests to Adzuna's REST endpoints and returning paginated job listings with standardized schema. Implements MCP resource and tool abstractions to expose Adzuna's search, filtering, and pagination capabilities as composable protocol operations that Claude and other MCP clients can invoke without direct API knowledge.
Unique: Wraps Adzuna's REST API as MCP protocol tools and resources, allowing Claude and other MCP clients to invoke job searches as first-class protocol operations rather than requiring custom HTTP client code or API credential management within the agent context.
vs alternatives: Eliminates boilerplate API integration code compared to direct REST client usage, and provides standardized MCP protocol interface that works across any MCP-compatible LLM client without tool-specific SDKs.
Supports filtering job results across multiple dimensions (keyword, location, salary range, job type, company) with geographic awareness, translating human-readable location names into Adzuna's location taxonomy and applying server-side filtering to reduce result set size before returning to client. Uses Adzuna's API query parameters to push filtering logic to the backend, minimizing data transfer and enabling efficient result ranking by relevance and salary.
Unique: Implements location-aware filtering by mapping human-readable location names to Adzuna's location taxonomy server-side, reducing client-side filtering logic and enabling efficient geographic scoping without requiring the MCP client to maintain location databases.
vs alternatives: More efficient than client-side filtering because filtering happens at the API level, reducing data transfer and enabling Adzuna's backend ranking algorithms to optimize result relevance within the filtered set.
Fetches complete job posting details for a specific job ID, including full description, company information, salary details, application URL, and posting metadata (date posted, last updated). Implements single-job lookup via Adzuna's job detail endpoint, providing rich context for downstream processing (summarization, matching, application automation) without requiring additional web scraping or external data enrichment.
Unique: Provides direct access to Adzuna's job detail endpoint through MCP, enabling rich job context retrieval without requiring the client to parse HTML or call multiple APIs, and supporting downstream LLM analysis of full job descriptions.
vs alternatives: Faster and more reliable than web scraping job postings, and provides structured metadata (salary, dates, company info) that would require NLP extraction from raw HTML.
Exposes Adzuna job search and detail operations as MCP resources and tools, allowing Claude and other MCP clients to discover and invoke job search capabilities through the MCP protocol's resource listing and tool calling mechanisms. Implements MCP server-side schema definitions for job search parameters and results, enabling type-safe tool invocation and automatic parameter validation before API calls.
Unique: Implements full MCP protocol compliance for job search operations, including resource listing, tool schema definitions, and parameter validation, enabling seamless integration with Claude and other MCP clients without custom tool definitions.
vs alternatives: Eliminates manual tool definition and parameter validation code compared to building custom Claude tools, and provides standardized MCP interface that works across any MCP-compatible client.
Supports paginated navigation through large job result sets using offset-based pagination (page number and results per page), returning metadata about total results, current page, and available pages to enable efficient result set traversal. Implements pagination parameters in search queries, allowing clients to iterate through results without loading entire result sets into memory.
Unique: Exposes Adzuna's offset-based pagination through MCP tool parameters, enabling clients to navigate result sets without implementing custom pagination logic or managing state across multiple API calls.
vs alternatives: Simpler to implement than cursor-based pagination for small-to-medium result sets, though less efficient for deep pagination compared to cursor-based alternatives like those used by modern job boards.
Implements error handling for Adzuna API failures (rate limits, network errors, invalid parameters) at the MCP layer, translating API errors into structured error responses that LLM agents can interpret and act upon. Includes retry logic with exponential backoff for transient failures and clear error messages for invalid search parameters.
Unique: Implements structured error handling and exponential backoff retry logic at the MCP layer, translating Adzuna API errors into actionable error responses that LLM agents can interpret and respond to
vs alternatives: Provides MCP-native error handling with automatic retry for transient failures, whereas direct API integration requires agents to implement error handling and retry logic in application code
Normalizes Adzuna API job results into a standardized schema that abstracts away API-specific field names and data types, ensuring consistent result structure across different Adzuna API versions or regional variants. Implements field mapping logic that translates Adzuna's raw response format into canonical job object schema with consistent field names, types, and optional fields.
Unique: Implements schema normalization at the MCP layer to abstract Adzuna API details, providing clients with a stable, canonical job object schema that isolates them from API changes or regional variants
vs alternatives: Provides schema abstraction that decouples clients from Adzuna API structure, whereas direct API integration exposes API schema details and requires clients to handle schema variations
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 adzuna-mcp at 23/100. adzuna-mcp leads on ecosystem, while Supabase is stronger on quality.
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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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