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Read-onlyIdempotent

"Job market trends" / "hiring trends in [category]" / "salary trends for [role]" / "is [field] hiring more / less than last year" — historical monthly time series for job volume and mean salary in a country, optionally filtered by location and category. Use for labor-market analysis, recession indicators, hiring-cycle research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthsNo1-100 (default 12)
countryYes
categoryNoAdzuna category tag (from `categories` tool)
locationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthNoAverage salary keyed by month, e.g. {"2026-07": 42537.34}. An OBJECT map, not an array — verified against a live response.

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already cover safety (readOnly, openWorld, idempotent, non-destructive). The description adds useful context about the output being a monthly time series of job volume and mean salary, but does not describe return format or limitations. No contradiction with annotations.

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 moderately sized and well-structured: example queries, then the core explanation, then use cases. It is slightly dense but each component adds value without redundancy.

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?

With an output schema and safety annotations present, the description sufficiently covers purpose, parameter examples, and intended use cases. It could be improved by explicitly contrasting with sibling tools like salary_histogram, but it is adequate for correct selection and invocation.

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

Parameters4/5

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

Schema description coverage is 50% (months and category). The description adds semantic value by clarifying 'country' as the geographic scope and providing concrete example values ('gb', 'us', 'New York') that help with parameter format. The 'months' parameter is left to the schema, which already documents it.

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

Purpose4/5

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

The description clearly states the tool returns 'historical monthly time series for job volume and mean salary in a country, optionally filtered by location and category,' identifying the resource and scope. It provides illustrative query examples but does not explicitly differentiate from sibling tools like salary_histogram.

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 gives explicit use cases: 'Use for labor-market analysis, recession indicators, hiring-cycle research.' It does not mention when not to use the tool or name alternative tools, so it misses the highest bar.

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.