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auxiliar-ai
by auxiliar-ai

get_scorecard

Retrieve a ranked leaderboard of providers for any web automation job, with measured performance metrics and dates, to compare all options.

Instructions

Full benchmark leaderboard for one job/verb: every scored provider on the auxiliar.ai gateway, best first, with raw measured metrics and run dates. Use to compare all options rather than be handed a pick.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobYesThe verb to rank. search = Web search: query → ranked results | scrape = One URL → clean page content/markdown, anti-bot handled | crawl = Site → enumerate and fetch many pages | extract_ai = Page → structured fields from an AI/natural-language schema | extract_rules = Page → structured fields via CSS/XPath rules | answer = Question → synthesized answer with cited sources | screenshot = URL → rendered page image | scrape_domain = Domain-specific scrapers for hard targets (e-commerce, social) | act = Declarative page interactions (click, fill, scroll) in one call | act_agent = Autonomous natural-language browser agent for multi-step goals | serp = Google SERP verticals (web, news, images, places, scholar) as structured JSON | parse = PDF/document → text (OCR where supported) | watch = URL → change detection / monitoring
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions returning a leaderboard with metrics and dates, but does not disclose whether the tool is read-only, has rate limits, requires authentication, or any other side effects. The description lacks transparency about the tool's behavior beyond its output.

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 two sentences: the first defines the output, the second gives usage guidance. Every word adds value with no repetition or fluff. It is front-loaded with the key information.

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 has one parameter with 100% schema coverage, no output schema, and no annotations, the description adequately explains the return data (leaderboard, metrics, dates). It could mention if there are any limits (e.g., number of providers) but is otherwise complete for a simple lookup tool.

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

Parameters3/5

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

The input schema coverage is 100% with detailed enum descriptions for the 'job' parameter. The description adds context about the output (leaderboard, metrics, dates) but does not add meaning to the parameter itself beyond the schema. Baseline 3 is appropriate.

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 specifies exactly what the tool does: provides a full benchmark leaderboard for one job/verb, listing providers with metrics and dates. It clearly distinguishes from sibling tools like get_provider and recommend_tools by focusing on comparison rather than selection.

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 explicitly states the use case: 'Use to compare all options rather than be handed a pick.' This tells the agent when to invoke this tool (when comparing providers) and implies not to use it when a single recommendation is needed.

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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