Skip to main content
Glama

quanticdata-mcp

by io.github.quantumproxies

list_collectors

Read-onlyIdempotent

List the ready-made Collectors: paid, versioned scrapers you run with a semantic input (keyword + location, place id, product id, domain…) instead of URLs — e.g. web_search, search_images, search_videos, keyword_ideas, amazon_search, amazon_product, ebay_search, aliexpress_search, linkedin_jobs, indeed_jobs, reddit_posts, youtube_search, youtube_channel, instagram_profile, tiktok_profile, tiktok_video, linkedin_profile, linkedin_company, zillow_search, zillow_property, app_store_apps, app_store_reviews, google_play_apps, google_maps_places, place_reviews, google_jobs, google_news, google_shopping, product_offers, hotels, google_flights, google_events, google_trends, google_autocomplete, google_lens, youtube_video, ebay_product, flipkart_search, idealista_search, kleinanzeigen_search, autotrader_search, github_repos, hacker_news, coingecko_coins, wikipedia_articles, yahoo_finance, stackoverflow, steam, npm_packages, sec_filings, defillama, wayback_machine, clinical_trials, certificate_transparency, wikidata, nvd_cve, openfda, openalex, pypi_packages, exchange_rates, gleif_lei, docker_hub, crates_io, world_bank, openlibrary_books, arxiv_papers, weather_forecast, whois_domain, dns_records, itunes_search, local_business_leads, site_contacts, company_profile, business_directory. Returns each collector's slug, input/output schema, example input, price per delivered result and current health. Billing is pay-per-success: only delivered rows are charged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoOptional category filter (e.g. 'local', 'ecommerce', 'jobs', 'news', 'travel', 'leads', 'finance', 'dev', 'gaming', 'osint', 'research', 'classifieds', 'knowledge')

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-open-world behavior. The description adds the return fields (slug, input/output schema, example input, price, health) and the pay-per-success billing model, which are not conveyed by the annotations.

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

Conciseness2/5

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

The description is a single unwieldy paragraph with dozens of example collector names, which buries the key facts. While the examples provide useful context, the length hurts scannability and the structure is not front-loaded beyond the first sentence.

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?

For a simple list tool with one optional filter and no output schema, the description adequately covers what is returned, the billing model, and the nature of the collectors. A formal return type or pagination behavior would be nice but is not essential here.

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 schema fully documents the optional category parameter with examples (100% coverage). The description adds nothing about the parameter itself, so baseline 3 applies.

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 opens with a specific verb and resource ('List the ready-made Collectors') and then defines what Collectors are with concrete examples. This clearly distinguishes it from siblings like run_collector, scrape, or list_parser_presets.

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 that Collectors are run with a semantic input instead of URLs, which implies when this tool is useful for discovering them. However, it does not explicitly state when to use this over alternatives or mention exclusions, leaving some inference to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All quanticdata-mcp toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: single scrape, batch scrape, crawl, search, dataset creation, parser lifecycle, proxy management, and SEO audit. Even the five status pollers are clearly differentiated by job type and their descriptions explicitly state which job they poll, so an agent can reliably select the right tool.

Naming Consistency4/5

Most names follow a verb-first pattern (create_dataset, generate_parser, run_collector, save_parser_preset, whitelist_ip) and listing tools consistently use the 'list_' prefix. However, a few are noun-first (parser_preset_stats, proxy_locations, collector_run_status) and the status polling tool for collectors breaks the otherwise consistent '<job>_status' convention ('collector_run_status' instead of 'run_collector_status').

Tool Count3/5

At 25 tools, the set is at the upper edge of the 'heavy' range. The tools all serve distinct functions, reflecting a broad platform covering scraping, crawling, search, datasets, parsers, proxies, and SEO, but the count borders on overwhelming for an agent, and some consolidation (e.g., a generic async job status endpoint) could reduce the surface.

Completeness4/5

The tool surface covers the core data-extraction lifecycle well: discovery (map, search), acquisition (scrape, batch, crawl), structured extraction (generate_parser, save_parser_preset, parser stats/heal), proxy management, and result aggregation (datasets, collectors). Notable gaps are the absence of any cancellation/abort mechanism for long-running async jobs and no way to delete a parser preset, but these are minor for most workflows.

Resources