Large on-chain token transfers (whale flows): 32 stock tokens + USDG above the USD threshold. direction: mint|burn|transfer. Mints/burns show the tokenized stock supply changing (issuance/redemption).
| Name | Required | Description | Default |
|---|---|---|---|
| hours | No | ||
| limit | No | ||
| ticker | No | ||
| min_usd | No | ||
| direction | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly/idempotent/destructive hints. The description adds meaningful behavioral context by explaining that mints/burns indicate tokenized stock supply changes (issuance/redemption). It does not describe pagination or return format but provides useful interpretive guidance beyond annotations.
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 two sentences, front-loaded with the core purpose, and every phrase adds context either about scope or direction semantics. 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?
With 5 parameters and no output schema, the description gives essential context but lacks details on threshold definition, how hours/limit affect results, and return shape. It is adequate for a simple query but would benefit from mentioning what the output contains.
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 hints at 'USD threshold' (min_usd) and direction enum values, but does not explicitly map all parameters. The parameter names (hours, limit, ticker) are self-explanatory, but the description adds only partial semantic value.
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 returns large on-chain token transfers ('whale flows') and scopes it to 32 stock tokens plus USDG above a USD threshold. It names the resource and verbs ('transfers', 'show') but does not explicitly differentiate from sibling tools like flow_summary.
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 tracking whale flows and notes direction semantics, but it does not provide explicit 'when to use' or alternative tool exclusions. No direct comparison with sibling tools is given, so the context is implied rather than stated.
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.
