lookup_useragents_top
Get the top 50 real-world browser User-Agent strings. Use for scraping agents that need realistic UAs.
Cost: $0.005–$0.05 USDC on Base per call.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query_string | No |
GoCreative Agent API
TDQS
A3.7/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must carry the full burden. It discloses cost ('$0.005–$0.05 USDC on Base per call'), which is a useful behavioral trait. However, it does not describe the return format, pagination, or any side effects, leaving 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 very short (two sentences plus a cost line) and front-loaded with the primary action. Every sentence adds value without redundancy.
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 no output schema, the description should explain the return format. It mentions 'top 50 real-world browser User-Agent strings' but not whether it returns an array or objects. The parameter is undocumented, making the tool harder to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one optional parameter 'query_string' with 0% description coverage. The description does not mention this parameter at all, leaving the agent to guess its purpose. This is a significant omission.
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 clearly states 'Get the top 50 real-world browser User-Agent strings', specifying the action and resource. It is distinct from sibling tools like lookup_user_agent_parse, and the verb 'Get' is precise.
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 explicitly says 'Use for scraping agents that need realistic UAs', providing clear context for when to use the tool. However, it does not mention when not to use it or alternatives, which keeps it from a perfect score.
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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TDQS
B3.2/5.0
Disambiguation2/5
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 Consistency4/5
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.
Tool Count1/5
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.
Completeness3/5
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.