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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes structured queries against the Adzuna Job Search API across 12 countries, supporting keyword matching, location-based filtering, salary range constraints, job type classification (full-time/part-time/contract/permanent), category tags, and relevance/date/salary sorting. The MCP server translates natural language search intents into parameterized API calls with country-specific currency handling and pagination support (up to 50 results per page), enabling AI assistants to discover job listings without direct API credential exposure.
Unique: Implements MCP protocol binding to Adzuna API with transparent country-currency mapping and category validation, allowing AI assistants to search job markets across 12 countries without managing API credentials or understanding regional data structures. Uses FastMCP framework for stdio-based transport and automatic tool schema generation from Python function signatures.
vs alternatives: Provides unified multi-country job search through a single MCP interface with local currency support, whereas direct Adzuna API usage requires developers to manage country-specific endpoints and currency conversions manually.
Retrieves salary histogram data from Adzuna API showing the distribution of compensation across job listings, bucketed by salary ranges. Supports filtering by keywords, location, and category to narrow the salary distribution context. Returns structured histogram data (salary ranges and frequency counts) enabling AI assistants to understand typical compensation bands, identify outliers, and advise users on market-competitive salary expectations without requiring statistical computation.
Unique: Wraps Adzuna's salary histogram endpoint in MCP protocol, exposing pre-computed salary distributions without requiring the AI assistant to aggregate or bucket raw job data. Supports contextual filtering (keywords, location, category) to generate histograms for specific job market segments.
vs alternatives: Eliminates need for AI assistants to fetch all job listings and compute histograms themselves; Adzuna API pre-aggregates this data server-side, reducing latency and API call volume compared to client-side aggregation approaches.
Manages Adzuna API credentials (App ID and App Key) via environment variables (ADZUNA_APP_ID and ADZUNA_APP_KEY) rather than hardcoding or prompting at runtime. The MCP server reads credentials from the environment at startup and uses them for all subsequent API requests. This pattern is compatible with MCP client configuration files (Claude Desktop, Cursor) that allow environment variables to be specified per MCP server instance, enabling secure credential isolation without exposing secrets in code or configuration files.
Unique: Integrates with MCP client configuration files (Claude Desktop, Cursor) to allow per-server environment variable specification, enabling secure credential isolation without requiring users to manage .env files manually. The server reads credentials at startup and validates them implicitly through the first API call.
vs alternatives: More secure than hardcoding credentials because secrets are not stored in code; more flexible than prompt-based credential entry because credentials can be configured per MCP server instance in client configuration files.
Provides a development mode (fastmcp dev) that launches an interactive browser-based UI for testing MCP tools without requiring a full MCP client setup. The development mode allows developers to invoke tools, inspect parameters, and view results in real-time, accelerating the development and debugging cycle. This is useful for testing tool schemas, validating API responses, and experimenting with filter combinations before deploying to production MCP clients.
Unique: FastMCP's dev mode provides a zero-setup interactive testing environment for MCP tools, eliminating the need to configure Claude Desktop or Cursor during development. The browser-based UI allows developers to test tools and inspect results without writing test code.
vs alternatives: Faster development iteration than testing through Claude Desktop because there's no client restart required; more accessible than command-line testing because the UI is visual and interactive.
Queries Adzuna's geodata endpoint to retrieve salary statistics broken down by geographic regions (cities, states, or postal areas) within a country. Enables comparative analysis of compensation across locations by filtering on keywords, location focus, and category. Returns structured geographic data with salary metrics per region, allowing AI assistants to help users understand regional salary variations and cost-of-living adjusted compensation differences.
Unique: Exposes Adzuna's geodata API through MCP, enabling AI assistants to perform multi-region salary comparisons without fetching and aggregating individual job listings. Supports contextual filtering to isolate salary data for specific job types or locations.
vs alternatives: Provides pre-aggregated regional salary data via API rather than requiring manual aggregation of job listings; faster and more efficient than client-side geographic bucketing of raw job data.
Retrieves historical salary trend data from Adzuna API showing how compensation has evolved over time (up to ~24 months of history). Supports filtering by keywords, location, and category to track salary trends for specific job markets. Returns time-series data with salary metrics per month, enabling AI assistants to identify salary growth patterns, seasonal variations, and long-term market compensation trends.
Unique: Wraps Adzuna's salary history endpoint to expose time-series compensation data, allowing AI assistants to analyze salary trends without computing historical aggregations. Supports configurable time windows (up to 24 months) and contextual filtering for trend analysis of specific job markets.
vs alternatives: Provides pre-computed historical salary trends via API rather than requiring manual collection and aggregation of historical job data; enables trend analysis without maintaining historical job listing archives.
Queries Adzuna API to identify and rank companies by number of open job positions within a country, with optional filtering by keywords, location, and category. Returns structured company data including company name and open position count, enabling AI assistants to identify major employers, understand hiring concentration, and help users target high-growth companies.
Unique: Exposes Adzuna's top companies endpoint through MCP, enabling AI assistants to identify major employers by hiring volume without fetching and aggregating individual job listings. Supports contextual filtering to discover employers in specific job markets.
vs alternatives: Provides pre-ranked employer lists via API rather than requiring manual aggregation of job listings by company; more efficient than client-side company deduplication and ranking.
Retrieves the complete set of valid job category tags for a specific country from Adzuna API. Returns structured category data (category IDs and labels) enabling AI assistants to validate category filters before executing job searches. This is a prerequisite capability — categories must be fetched first to ensure search queries use valid category tags, preventing failed searches due to invalid category parameters.
Unique: Provides country-specific category enumeration as a prerequisite step before job searches, ensuring AI assistants can validate filter parameters and avoid invalid category errors. Caches category data to reduce API calls for repeated searches in the same country.
vs alternatives: Eliminates trial-and-error category discovery by exposing valid categories upfront; prevents failed searches due to invalid category parameters compared to approaches that discover categories through search error responses.
+4 more capabilities
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-job-search-mcp at 35/100.
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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
+1 more capabilities