Scrape Booking.com hotels (price, rating, location). Use for travel-research agents.
Example call: {"hotel_or_query": "marriott+new+york"}
Cost: $0.005–$0.05 USDC on Base per call.
| Name | Required | Description | Default |
|---|---|---|---|
| hotel_or_query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions cost and example call but does not disclose rate limits, authentication needs, error behavior, or the fact that scraping may be blocked or delayed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no wasted words. Purpose, example, and cost are front-loaded. Ideal length for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so description should explain return values. It lists fields (price, rating, location) but not structure or error handling. Adequate for a trivial tool but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter exists with 0% schema coverage. The description adds an example value ('marriott+new+york') which hints at acceptable formats, but it does not clarify whether hotel name, query, or ID is expected.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'scrape' and resource 'Booking.com hotels' with specific fields (price, rating, location). It distinguishes from sibling scrape tools by naming the source and intended use for travel-research agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use for travel-research agents', which gives a context but no explicit when-to-use or when-not-to-use. It does not mention alternatives among siblings like scrape_airbnb or scrape_tripadvisor.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The set is riddled with near-duplicates: lookup_reddit/scrape_reddit, lookup_wikipedia/scrape_wikipedia, lookup_dockerhub/scrape_dockerhub, lookup_steam/scrape_steam, enrich_googlereviews/enrich_reviews, lookup_ip/lookup_ipinfo, and multiple crypto-pricing tools (lookup_crypto, lookup_coingecko, bundle_crypto_360, scrape_binance, scrape_coinbase). Descriptions try to differentiate with phrases like 'heavier than' or 'same domain but with full thread parsing,' but the boundaries are fuzzy and an agent can easily pick the wrong one.
Naming follows a fairly consistent prefix-based snake_case pattern (lookup_, scrape_, enrich_, bundle_, search_, ai_, data_) where the prefix denotes action weight and the noun identifies the target. Minor deviations exist: posts_x, ai_ask/pro/ultra (model-tier names instead of resources), sslstatus (missing underscore), and lookup_useragents_top are slightly off-pattern.
172 tools is an extreme count, far beyond even the 50+ floor for a score of 1. This floods the agent's context and tool-selection space, making every call require a search through a massive list. While aggregation servers can justify more tools, this volume is unmanageable and every tool must be evaluated by the agent.
The surface is extraordinarily broad but unevenly deep: many sources have both a light lookup and a heavy scrape variant, while other areas have just a single shallow endpoint. There is no coherent domain with complete lifecycle coverage, and despite the huge catalog, common capabilities are still absent. The breadth prevents obvious gaps, but depth and coherence suffer.