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folathecoder

Adzuna Jobs MCP Server

by folathecoder

search_jobs

Search job listings across 12 countries with filters for location, salary, contract type, and keywords to find relevant employment opportunities.

Instructions

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"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countryYes
keywordsNo
locationNo
pageNo
results_per_pageNo
salary_minNo
salary_maxNo
full_timeNo
part_timeNo
contractNo
permanentNo
categoryNo
sort_byNo
max_days_oldNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure and does so comprehensively. It explains: salary figures are ANNUAL in LOCAL CURRENCY, keywords are OR'd and case-insensitive, location uses fuzzy matching, pagination starts at page 1, salary filters exclude jobs without listed salaries, and it documents specific error conditions (rate limits, authentication failures, invalid inputs). This goes well beyond basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Args, Returns, Errors) and uses bullet points effectively. While comprehensive, it's appropriately sized for a complex tool with 14 parameters. Some redundancy exists (salary currency mentioned multiple times), but overall the structure helps navigation. Every sentence adds value, though it could be slightly more concise in places.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (14 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is remarkably complete. It covers purpose, usage guidelines, detailed parameter semantics, behavioral traits, error conditions, and includes a comprehensive example response. The output schema handles return value structure, allowing the description to focus on semantic context. This provides everything needed for effective tool use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for 14 parameters, the description fully compensates by providing detailed semantic information for every parameter. Each parameter gets clear explanations: country includes supported codes and currency implications, keywords explains OR logic and examples, location describes fuzzy matching and examples, page clarifies starts at 1, salary_min notes exclusion behavior, category references get_categories prerequisite, etc. The description adds substantial value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search for jobs on Adzuna across 12 supported countries.' It specifies the verb ('search'), resource ('jobs'), and scope ('across 12 supported countries'), distinguishing it from sibling tools like get_categories or get_top_companies. The description immediately establishes this is a search tool for job listings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool versus alternatives. It mentions: 'For remote jobs, include "remote" in keywords' (instead of using location), 'Call get_categories(country) first to get valid tags' (prerequisite for category parameter), and lists sibling tools like get_categories that should be used first. It also explains when to leave parameters empty (location for country-wide search).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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