Real-time price momentum for a tokenized stock on Robinhood Chain: price change over 5m/1h/6h/24h, per-window onchain volume, a volume-surge ratio (last hour vs the 24h hourly average), and an accelerating flag. Send { ticker }. Catch a tokenized stock breaking out intraday. [x402 paid tool — price $0.005; POST /api/rh/momentum]
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Robinhood tokenized-stock ticker, e.g. NVDA |
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 paid nature with price ($0.005) and API endpoint, which is useful. However, it doesn't mention other behavioral traits like rate limits, latency, or whether it's a read-only operation. Still, the paid disclosure adds significant transparency.
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 extremely concise: two sentences plus a note on cost/endpoint. Every part is useful, no fluff. It front-loads the purpose and key metrics, making it easy to scan.
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 has only one parameter and no output schema, the description adequately covers input (ticker) and output (list of metrics). It also provides context on the use case (intraday breakout) and cost. For a simple tool, this is 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?
Schema coverage is 100% (one parameter fully described with example). The description adds marginal value by repeating 'Send { ticker }' and reinforcing the example. Baseline 3 is appropriate since the schema already does the heavy lifting.
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 real-time price momentum for tokenized stocks on Robinhood Chain, listing specific metrics (price change over intervals, volume, surge ratio, accelerating flag). It distinguishes from sibling tools by focusing on momentum, a unique niche among many analytics tools.
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 by saying 'Catch a tokenized stock breaking out intraday,' which suggests when to use it. However, it does not explicitly state when not to use it or mention alternative tools. Guidance is implied, not explicit.
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.