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pindaroli

mcp-server-subito-scraper

by pindaroli

subito_scrape_by_url

Scrape classified ads from any Subito.it search URL. Provide the full search URL to retrieve listings with optional limits on number of items and timeout.

Instructions

Scrapes classified ads directly from a Subito.it search URL using Apify Actor (azzouzana/subito-scraper-pro-by-search-url)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokenNoOptional Apify API Token (overrides APIFY_TOKEN environment variable)
maxItemsNoMaximum number of ads/listings to retrieve (default: 30)
searchUrlYesFull Subito.it search URL to scrape (e.g. "https://www.subito.it/annunci-italia/vendita/usato/?q=iphone+15")
timeoutSecsNoTimeout in seconds for Apify Actor execution (default: 300)
Behavior3/5

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

With no annotations provided, the description must fully disclose behavioral traits. It mentions the use of an Apify Actor and provides parameters like timeout, which hints at asynchronous behavior. However, it does not disclose whether the tool is synchronous or asynchronous, if it returns results directly or only initiates a run, or what happens on failure (e.g., invalid URL, Actor timeout). The description is adequate but leaves gaps.

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 a single, compact sentence that conveys the core purpose and implementation. It wastes no words and is easy to parse. This is exemplary conciseness.

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

Completeness2/5

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

Given the tool has no output schema, 4 parameters, and no annotations, the description is too brief. It does not explain the return format (e.g., what the scraped data looks like), error behavior, or how to handle the Apify Actor lifecycle (e.g., need to check status or get dataset items separately). The presence of sibling tools like 'apify_check_status' and 'subito_get_dataset_items' suggests a pipeline, but the description does not connect them.

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 coverage is 100% (all 4 parameters have descriptions). The tool description adds no additional parameter details, but the schema itself is complete and clear. The baseline is 3 per the rubric; the description includes the full search URL format example, which adds marginal value. Thus a score of 4 is appropriate.

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 action ('scrapes classified ads'), the specific source ('directly from a Subito.it search URL'), and the implementation ('using Apify Actor'). The name 'subito_scrape_by_url' reinforces this. While the purpose is clear, it does not explicitly distinguish from siblings like 'subito_search' which might serve a similar but different purpose.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'subito_search' or 'subito_get_dataset_items'. It does not mention prerequisites (e.g., valid search URL format), when not to use it, or what to do after scraping (e.g., check status or retrieve items). The agent is left to infer usage from context.

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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