Premium/discount arbitrage signal: compares a tokenized stock's live Robinhood Chain price against the real-world equity price and returns the premium/discount % plus a buy-onchain/sell-equity signal. Send { ticker }. The core edge for tokenized-equity arb agents trading 24/7 vs market hours. [x402 paid tool — price $0.005; POST /api/rh/premium]
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Stock/ETF ticker, e.g. AAPL |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully handles transparency. It discloses the tool is paid ($0.005) and provides the API endpoint. It does not describe error handling or rate limits, but the core behavioral aspects (cost, action) are covered.
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?
Two concise sentences plus a succinct paid-tool note. Every sentence adds value, front-loaded with purpose and outputs. No wasted words.
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 one-parameter tool with no output schema, the description adequately covers input, function, output (premium/discount %, signal), and cost. It lacks output structure details but that is acceptable given no output schema.
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% with a description for ticker. The description adds 'Send { ticker }' which reiterates the input but does not enrich beyond the schema. Baseline of 3 is appropriate.
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 computes a premium/discount arbitrage signal by comparing Robinhood Chain price to real-world equity price, returning percentage and trade signal. It highlights the 24/7 advantage, effectively distinguishing it from siblings like rh_arb or rh_basis.
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 indicates usage by sending a ticker and mentions the tool's edge for tokenized-equity arb agents. While it provides context, it lacks explicit when-not-to-use or alternative tool references, but the context is clear enough.
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.