Onchain buy/sell pressure for a tokenized stock on Robinhood Chain: buys vs sells, buy % of trades, and average trade size (a whale-vs-retail proxy) across 5m/1h/6h/24h. Send { ticker }. See who is accumulating or dumping the token right now. [x402 paid tool — price $0.005; POST /api/rh/whale-flow]
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Robinhood tokenized-stock ticker, e.g. TSLA |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It discloses the tool is paid ($0.005 cost) and provides the endpoint, but omits details on data freshness, rate limits, or request-response size. The description is insufficient for a paid tool.
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 a single sentence with a clear structure: function, metrics, timeframes, input example, and usage context (paid tool note). It is front-loaded with the main purpose, though the pricing and endpoint note could be integrated more smoothly.
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 the tool's simplicity (one parameter, no output schema), the description covers input requirements, output metrics, timeframes, and cost. It does not explain output format, but for a list of metrics, this is acceptable. Lacks details on data source or update frequency.
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 covers 100% of parameters with a description of 'ticker'. The description adds value by providing an example ('e.g. TSLA'), clarifying the token type ('tokenized-stock'), and specifying the chain context ('Robinhood Chain'), which enriches the schema definition.
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's function: analyzing onchain buy/sell pressure for tokenized stocks on Robinhood Chain, specifying metrics (buys vs sells, buy %, avg trade size) and timeframes (5m/1h/6h/24h). It distinguishes from siblings by focusing on whale flow and accumulation/dumping detection.
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 implies usage for checking current whale activity ('See who is accumulating or dumping the token right now') but does not explicitly state when to use vs. alternatives (e.g., rh_stock, rhchain_balance). No exclusions or comparisons with sibling tools are provided.
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.
Tools are organized by domain prefix (e.g., 'crypto_', 'rh_', 'snipe_'), which helps distinguish between areas. Within each domain, they serve distinct purposes, though some overlap between domains exists (e.g., price data appears in multiple groups). Overall, an agent can navigate effectively.
All tool names follow a consistent snake_case pattern with a domain prefix and a verb_noun combination (e.g., 'compliance_risk', 'rh_stock', 'snipe_honeypot'). This makes the API predictable and easy to explore.
With 159 tools, the server is extremely large. While the broad scope of web3 and utility functions justifies many tools, the count is significantly above the typical range for a coherent toolkit, potentially overwhelming agents and increasing selection error.
The toolkit covers a wide range of web3 operations: crypto, DeFi, compliance, safety, scheduling, memory, etc. There are no obvious major gaps for its intended purpose, though some niche areas might be missing.