Scrape an Amazon product (ASIN) or search query — title, price, rating, reviews, image. Use for e-commerce price tracking and competitive intel.
Example call: {"asin_or_query": "B08N5WRWNW"}
Cost: $0.005–$0.05 USDC on Base per call.
| Name | Required | Description | Default |
|---|---|---|---|
| asin_or_query | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the cost range ($0.005-$0.05) and output fields, but lacks details on rate limits, authentication needs, or failure scenarios. This is adequate but not comprehensive.
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?
The description is concise with three sentences plus an example and cost line. It is front-loaded with purpose, then example, then cost. Every sentence adds value with no redundancy.
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?
For a simple one-parameter tool with no output schema, the description covers purpose, parameter, output fields, example, and cost. It could be more complete by mentioning the return format (e.g., JSON) or potential limitations, but it is largely sufficient.
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?
The schema has one string parameter with 0% description coverage. The description clarifies it can be an ASIN or search query and provides an example, adding significant meaning beyond the schema. However, it does not specify format constraints for search queries.
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 tool scrapes Amazon product or search query and lists specific fields (title, price, rating, reviews, image). It also specifies the use case for e-commerce price tracking and competitive intel, distinguishing it from sibling tools targeting other platforms.
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 provides clear usage context (price tracking, competitive intel) through an example. However, it does not explicitly mention when not to use it or suggest alternatives, though the sibling tools list makes differentiation implicit.
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.