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Glama
pindaroli

mcp-server-subito-scraper

by pindaroli

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
tokenNoYour Apify API token (passed as CLI argument --token)
APIFY_TOKENNoYour Apify API token (set as environment variable)

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
subito_scrape_by_urlB

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

subito_searchB

Searches Subito.it for ads by keywords, category, region, price range, and shipping, then scrapes the results via Apify

subito_get_dataset_itemsC

Fetches scraped items from a previously generated Apify dataset ID

apify_check_statusB

Checks the status of the Apify account and validates the API token

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

B3.3/5.0

Scored across 4 tools

Disambiguation4/5

Each tool targets a distinct aspect: scraping by URL vs. by search parameters, checking account status, and retrieving dataset items. There is slight potential confusion between subito_scrape_by_url and subito_search, but their descriptions clarify the difference (URL vs. keyword-based).

Naming Consistency4/5

Tool names use a consistent verb_noun pattern with a domain prefix (subito_). Names are descriptive and follow a predictable structure, though subito_search could optionally be subito_search_by_keywords for perfect parallelism with subito_scrape_by_url.

Tool Count5/5

Four tools are appropriate for a focused scraping server: two scraping methods, one status check, and one data retrieval tool. Each tool has a clear purpose and no unnecessary bloat.

Completeness3/5

The tool surface covers scraping, search, status checking, and data retrieval, but lacks ability to list or manage datasets (e.g., delete old datasets). An agent scraping multiple times may accumulate datasets without cleanup, which is a notable gap.

Maintenance

ActivitySlowing
ResponsivenessNo issues