KYB / counterparty-risk verdict for a company in ONE call: sanctions + PEP + watchlist screen (OFAC/EU/UK/UN) + entity risk score + KYB registry dossier + legal/case-law exposure + firmographics. Use for customer/vendor onboarding, merchant underwriting, compliance, and investment due diligence.
Example call: {"name": "Stripe"}
Cost: $0.005–$0.05 USDC on Base per call.
| Name | Required | Description | Default |
|---|---|---|---|
| name | 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 the tool aggregates data in 'ONE call' and gives cost transparency, but lacks details on read-only nature, error handling, data freshness, or any side effects. The cost disclosure adds value, but overall behavioral traits are minimally addressed.
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: a clear purpose statement, list of included data, use cases, example, and cost. No redundant sentences; information is front-loaded and well-organized. Every sentence adds value.
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?
Given no output schema, the description explains what data the bundle includes (sanctions, risk score, etc.), which is sufficient for understanding output scope. However, it does not describe the output structure or how results are formatted, which would aid agent usage. Still, for a single-parameter tool, it is reasonably complete.
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?
With 0% schema description coverage, the description compensates by naming the single parameter 'name' and providing an example (Stripe), clearly indicating it expects a company name. No validation or format details, but the simplicity of one string parameter makes this adequate.
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 provides a KYB/counterparty-risk verdict for a company, listing specific components (sanctions, PEP, risk score, etc.) and use cases (onboarding, underwriting, compliance). It distinguishes itself from sibling tools that target other entities (e.g., bundle_crypto_360).
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 explicitly recommends use cases like customer/vendor onboarding and compliance. It provides an example call and cost range. However, it does not explicitly exclude scenarios where simpler tools might suffice, which would enhance guidance.
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.