Search for jobs on Adzuna across 12 supported countries.

IMPORTANT: All salary figures are ANNUAL amounts in LOCAL CURRENCY.

Args: country: ISO 3166-1 alpha-2 country code. Determines job market AND currency. Supported: "gb" (UK/GBP), "us" (USA/USD), "de" (Germany/EUR), "fr" (France/EUR), "au" (Australia/AUD), "nz" (New Zealand/NZD), "ca" (Canada/CAD), "in" (India/INR), "pl" (Poland/PLN), "br" (Brazil/BRL), "at" (Austria/EUR), "za" (South Africa/ZAR)

keywords: Space-separated search terms matched against job title and description.
    - Terms are OR'd together (more matches = higher ranking)
    - Case insensitive
    - No boolean operators (AND/OR/NOT not supported)
    Examples: "python developer", "machine learning engineer", "senior react"

location: Geographic filter with fuzzy matching.
    Accepts: city names, regions, postal code prefixes.
    Examples: "London", "Manchester", "SW1" (UK), "New York", "10001" (US)
    Leave empty for country-wide search. For remote jobs, include "remote" in keywords.

page: Page number for pagination (starts at 1, not 0).

results_per_page: Results per page (default 10, max 50).

salary_min: Minimum ANNUAL salary filter in LOCAL CURRENCY (e.g., 50000 not 50).
    Note: Jobs without listed salaries are excluded when using this filter.

salary_max: Maximum ANNUAL salary filter in LOCAL CURRENCY.

full_time: Set True to show ONLY full-time jobs.

part_time: Set True to show ONLY part-time jobs.

contract: Set True to show ONLY contract/freelance jobs.

permanent: Set True to show ONLY permanent positions.

category: Job category tag from get_categories tool.
    Common tags: "it-jobs", "engineering-jobs", "finance-jobs", "sales-jobs"
    IMPORTANT: Call get_categories(country) first to get valid tags.

sort_by: Sort order - "date" (newest first), "salary" (highest first),
    "relevance" (best match, default).

max_days_old: Maximum age of listings in days (e.g., 7 for last week).

Returns: dict: Search results containing: - count (int): Total matching jobs (for pagination) - results (list): Job listings, each with: - id: Unique job identifier - title: Job title - company.display_name: Employer name - location.display_name: Job location - description: Truncated job description (~150 chars) - redirect_url: URL to apply (via Adzuna redirect) - created: ISO 8601 posting date - salary_min, salary_max: Annual salary range (may be null) - salary_is_predicted: "1" if Adzuna estimated the salary from job description, "0" if the employer explicitly listed the salary. Predicted salaries are less reliable for negotiation. - contract_type: "permanent", "contract", etc. - contract_time: "full_time", "part_time" - category.tag: Category identifier

Example response: { "count": 523, "results": [{ "id": "4123456789", "title": "Senior Software Engineer", "company": {"display_name": "Tech Corp"}, "location": {"display_name": "London"}, "salary_min": 70000, "salary_max": 90000, "redirect_url": "https://www.adzuna.co.uk/..." }] }

Errors: - Invalid country code: "API Error 400: Invalid country" - Invalid category: "API Error 400: Invalid category tag" - Rate limit exceeded: "API Error 429: Too many requests" - Authentication failure: "API Error 401: Invalid credentials"

Get valid job category tags for a specific country.

PURPOSE: Use this BEFORE search_jobs to get valid 'category' parameter values. Category tags are COUNTRY-SPECIFIC - always use the same country code here as you will in search_jobs.

Args: country: ISO 3166-1 alpha-2 country code. Supported: "gb", "us", "de", "fr", "au", "nz", "ca", "in", "pl", "br", "at", "za"

Returns: dict: Contains "results" array of category objects: - tag: Use THIS value in search_jobs category parameter (e.g., "it-jobs") - label: Human-readable name for display (e.g., "IT Jobs")

Common category tags (vary by country): - "it-jobs": Technology, software, IT support - "engineering-jobs": Mechanical, electrical, civil - "finance-jobs": Accounting, banking, financial services - "sales-jobs": Sales, business development - "healthcare-nursing-jobs": Medical, nursing - "admin-jobs": Administration, office support - "marketing-jobs": Marketing, PR, communications

Example response: { "results": [ {"tag": "it-jobs", "label": "IT Jobs"}, {"tag": "engineering-jobs", "label": "Engineering Jobs"}, {"tag": "finance-jobs", "label": "Accounting & Finance Jobs"} ] }

Usage: categories = get_categories("gb") search_jobs(country="gb", category="it-jobs") # Use tag, not label

Errors: - Invalid country code: "API Error 400: Invalid country" - Rate limit exceeded: "API Error 429: Too many requests" - Authentication failure: "API Error 401: Invalid credentials"

Get salary distribution histogram for jobs matching search criteria.

PURPOSE: Understand salary ranges in a job market. Useful for: - "What's the typical salary for X role?" - Salary negotiation research - Market positioning analysis

IMPORTANT: Only includes jobs WITH listed salaries. Many jobs don't list salary.

Args: country: ISO 3166-1 alpha-2 country code. Determines currency. Supported: "gb", "us", "de", "fr", "au", "nz", "ca", "in", "pl", "br", "at", "za"

keywords: Search terms to filter jobs (e.g., "software engineer", "data scientist").

location: Location filter (e.g., "London", "New York").

category: Category tag from get_categories (e.g., "it-jobs").

Returns: dict: Contains "histogram" object with salary buckets: - Keys: Salary values as strings (e.g., "30000", "35000") - Values: Number of jobs at that salary point

Example response: { "histogram": { "25000": 89, "30000": 234, "35000": 456, "40000": 567, "45000": 489, "50000": 378, "55000": 245, "60000": 167 } }

How to interpret: - Keys are ANNUAL salaries in LOCAL CURRENCY - Buckets are typically £5,000 / $5,000 increments - Peak of distribution = most common salary - To find median: find salary where cumulative count reaches 50%

Errors: - Invalid country code: "API Error 400: Invalid country" - Invalid category: "API Error 400: Invalid category tag" - Rate limit exceeded: "API Error 429: Too many requests" - Authentication failure: "API Error 401: Invalid credentials"

Get top employers currently hiring, ranked by number of open positions.

PURPOSE: Identify major employers in a field. Useful for: - "Which companies are hiring the most engineers?" - Researching potential employers - Understanding market leaders by hiring volume

NOTE: Shows hiring VOLUME, not company quality. Smaller great companies may not appear.

Args: country: ISO 3166-1 alpha-2 country code. Supported: "gb", "us", "de", "fr", "au", "nz", "ca", "in", "pl", "br", "at", "za"

keywords: Filter to specific roles (e.g., "software engineer", "data scientist").

location: Location filter (e.g., "London" for London-based employers).

category: Category tag from get_categories (e.g., "it-jobs").

Returns: dict: Contains "leaderboard" array of company objects: - canonical_name: Company name (normalized) - count: Number of open positions - average_salary: Average salary across listings (may be null)

Example response: { "leaderboard": [ {"canonical_name": "NHS", "count": 1245, "average_salary": 42000}, {"canonical_name": "Amazon", "count": 567, "average_salary": 65000}, {"canonical_name": "Google", "count": 234, "average_salary": 95000} ] }

Notes: - Ranked by job count (most positions first) - Typically returns 10-20 companies - average_salary is ANNUAL in LOCAL CURRENCY (may be null)

Errors: - Invalid country code: "API Error 400: Invalid country" - Invalid category: "API Error 400: Invalid category tag" - Rate limit exceeded: "API Error 429: Too many requests" - Authentication failure: "API Error 401: Invalid credentials"

Get salary and job count data broken down by geographic region.

PURPOSE: Compare salaries and job availability across areas. Useful for: - "Where are the highest paying X jobs?" - "Which cities have the most opportunities?" - Relocation decisions

Args: country: ISO 3166-1 alpha-2 country code. Supported: "gb", "us", "de", "fr", "au", "nz", "ca", "in", "pl", "br", "at", "za"

keywords: Filter to specific roles (e.g., "software engineer").

location: Focus on a region for sub-area breakdown.
    - Empty: National breakdown (London, Manchester, etc.)
    - "London": Breakdown within London (City, Canary Wharf, etc.)

category: Category tag from get_categories (e.g., "it-jobs").

Returns: dict: Contains "locations" array of region objects: - location.display_name: Region name - location.area: Geographic hierarchy array - count: Number of jobs in region - average_salary: Average salary (may be null)

Example response: { "locations": [ { "location": {"display_name": "London", "area": ["UK", "London"]}, "count": 15678, "average_salary": 62000 }, { "location": {"display_name": "Manchester", "area": ["UK", "Manchester"]}, "count": 3456, "average_salary": 48000 } ] }

Notes: - Results ordered by job count (most jobs first) - average_salary is ANNUAL in LOCAL CURRENCY - Typically returns 10-20 top regions

Errors: - Invalid country code: "API Error 400: Invalid country" - Invalid category: "API Error 400: Invalid category tag" - Rate limit exceeded: "API Error 429: Too many requests" - Authentication failure: "API Error 401: Invalid credentials"

Get historical salary trends over time for matching jobs.

PURPOSE: Analyze how salaries have changed. Useful for: - "Are X salaries going up or down?" - Trend analysis for negotiations - Market timing for job searches

Args: country: ISO 3166-1 alpha-2 country code. Supported: "gb", "us", "de", "fr", "au", "nz", "ca", "in", "pl", "br", "at", "za"

keywords: Filter to specific roles (e.g., "software engineer").

location: Location filter (e.g., "London").

category: Category tag from get_categories (e.g., "it-jobs").

months: Number of months of history (default 12, max ~24).
    - 6: Recent trend
    - 12: Year-over-year comparison
    - 24: Longer-term view

Returns: dict: Contains "month" array of data points: - month: Year-month string (YYYY-MM format) - salary: Average salary that month (annual, local currency)

Example response: { "month": [ {"month": "2024-01", "salary": 52000}, {"month": "2024-02", "salary": 52500}, {"month": "2024-03", "salary": 53000} ] }

How to analyze: - Compare first vs last month for overall change - Calculate % change: ((last - first) / first) * 100 - Look for consistent direction vs volatility

Errors: - Invalid country code: "API Error 400: Invalid country" - Invalid category: "API Error 400: Invalid category tag" - Rate limit exceeded: "API Error 429: Too many requests" - Authentication failure: "API Error 401: Invalid credentials"

Get the current Adzuna API version.

Returns: API version information