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Adzuna

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds that Anthropic calls pass through directly and you pay Anthropic, which is helpful behavioral context beyond annotations. It does not contradict annotations.

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

Conciseness5/5

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

The description is three sentences, each carrying essential information: purpose, default behavior, output format, and use cases. No redundancy or unnecessary detail.

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?

Despite no output schema, the description outlines the return structure (per-model {score, confidence, signals, raw_response} + combined view). For a probe tool with good annotations and 4 parameters, this is sufficient for an agent to use correctly.

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 100%, so baseline is 3. The description adds meaning by explaining parameter roles (e.g., default model, optional context for disambiguation) and the purpose of _apiKey. This adds value beyond the 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 uses a specific verb ('probe'), names the resource ('one or more LLMs'), and states the output (visibility score 0-100 per model). It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on general brand visibility rather than competitive scanning.

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 explains the default model (Workers AI) and how to add Anthropic with an API key. It notes cost implications for Anthropic. While it doesn't explicitly state when to avoid the tool or list alternatives, it provides sufficient context for typical use cases.

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