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Adzuna

by io.github.pipeworx-io

Salary Histogram

salary_histogram
Read-onlyIdempotent

"What does [role] pay" / "salary distribution for [job]" / "average salary for [title] in [location]" / "wage histogram for [profession]" — wage distribution histogram for jobs matching a query. Use for salary benchmarking, comp negotiation research, market-rate analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whatNo
whereNo
countryYes
location_filterNoAdzuna location id (e.g. "London")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
currencyNoSalary currency
histogramNoSalary distribution buckets

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already indicate safe, read-only, idempotent behavior. The description adds no further behavioral details (e.g., response format, pagination, rate limits). It does not contradict annotations, but adds minimal transparency value beyond them.

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 a single concise sentence, front-loaded with example queries. Every part earns its place, though the dash transition is slightly abrupt. Overall efficient and to the point.

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

Completeness4/5

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

Given the tool's simplicity (4 parameters, output schema exists), the description covers the essential purpose and use cases. It does not explain return values, but the output schema handles that. Minor gap: no mention of required parameters or country dependency, but schema marks them.

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

Parameters2/5

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

Schema description coverage is only 25% (only 'location_filter' has a description). The description text does not explain parameters beyond the example queries in the schema. With low coverage, the description should compensate, but it adds no parameter-level detail, leaving the agent to infer from examples.

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 returns a wage distribution histogram for jobs matching a query, with concrete example phrases and use cases (salary benchmarking, comp negotiation, market-rate analysis). It distinguishes the tool's specific function from general search tools.

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

Usage Guidelines4/5

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

The description provides clear use cases ('for salary benchmarking, comp negotiation research, market-rate analysis'), implicitly guiding when to use it. However, it does not explicitly mention alternatives or when not to use this tool, lacking full differentiation from sibling tools.

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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TDQS

A3.7/5.0
Disambiguation3/5

Most tools are clearly distinct, but ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research overlap with the base router, and the polymarket_* family contains several scanning/arbitrage tools with fuzzy boundaries. The long descriptions help, but an agent could easily call the wrong variant.

Naming Consistency3/5

There are coherent clusters (pipeworx_*, polymarket_*, ask_pipeworx_*, bare Adzuna verbs), but the overall server mixes snake_case, bare nouns, compound names, and -_prefixed names without a unifying convention. Some tools like compare_entities, entity_profile, and scan_dependency follow a descriptive style that does not match the verb_ noun pattern used elsewhere.

Tool Count2/5

37 tools is well above the 25-tool threshold, and the server named 'Adzuna' includes far more than job-search functionality: prediction markets, memory, subscriptions, npm dependency checks, AI visibility probes, and llms.txt generation. The count feels like a bundled mega-platform rather than a focused job-data server.

Completeness3/5

For a job-search-focused server, the Adzuna tools cover search, categories, history, regional stats, salary histograms, and top companies, but there is no direct job-detail or application workflow. For the broader Pipeworx research surface, coverage is very thorough, so the main completeness problem is the lack of a clear unified domain rather than a specific missing operation.

Resources