Hourly funding rates on tokenized-stock perps (Arcus + Lighter). Without params: latest value per venue+ticker pair (funding_apr_pct = annualized %). With ticker: history. Tokens trade 24/7 while the stock market does not — weekend/overnight funding is the price of that mismatch.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| venue | No | ||
| ticker | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds context about data frequency and market hours (24/7 tokenized stocks vs stock market) and explains that funding_apr_pct is annualized. However, it does not describe return format details or pagination behavior, so it adds some but not rich behavioral context.
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 four short sentences, front-loaded with the core purpose, and every sentence adds value: purpose, no-param behavior, with-ticker behavior, and market context. No unnecessary repetition or fluff.
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 simple read-only tool with 3 optional params and no output schema, the description covers core behavior, parameter effects, and provides market context. It could mention limit behavior or default response shape, but the essential information for invoking the tool is present. Given low complexity, this is fairly 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 description coverage is 0%, so the description must compensate. It explains ticker semantics (history) and mentions venue pair context, but does not explain the limit parameter at all. It adds partial meaning beyond the schema but leaves gaps, so a score 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 provides hourly funding rates on tokenized-stock perps, names the venues (Arcus + Lighter), and distinguishes behavior based on parameters (latest vs history). This is a specific verb+resource with clear scope, distinguishing it from sibling tools like funding_carry_signals.
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 gives clear usage context: without params returns latest values per venue/ticker pair; with ticker returns history. It does not explicitly name alternatives or when-not-to-use, but the parameter-dependent behavior is clearly conveyed. This is clear context without exclusions.
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.
Most tools have clearly distinct purposes, with detailed descriptions that separate e.g. hood_basis_snapshot from hood_basis_history and perp_spot_basis. A few clusters like flow_summary vs token_flows and multiple gap/basis tools could still cause slight ambiguity, but the descriptions are strong enough to guide correct selection.
All tool names follow a consistent snake_case pattern with domain prefixes (hood_, agent_, competition_, partner_), and verbs are used predictably (report_, execute_, get_, etc.). There is no mixing of conventions or vague generic names.
39 tools is on the high side, but the server covers a very broad domain (market data, signals, trading execution, competition, partner APIs, agent reporting). While many tools earn their place, a few could be consolidated (e.g., gap/basis variants), making it feel heavier than necessary for typical usage.
The surface covers the core lifecycle: market data, signals, paper/real/partner trading, position tracking, and performance reporting. Minor gaps exist (e.g., no explicit wallet balance or order cancellation), but the core workflows are well-covered and derived endpoints fill most needs.
