Overnight and weekend premium/discount statistics for all tickers: average, min/max and latest value (bps). Tokens trade 24/5, so this measures the drift that builds up while NASDAQ is closed.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds value by explaining what the statistics measure (premium/discount drift) and the specific metrics included, which goes beyond the annotation metadata. No contradictions.
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 sentences with all essential information front-loaded. The first sentence states the resource and metrics; the second explains the rationale. No fluff, perfectly sized.
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?
The tool is simple (one optional parameter, no output schema), and the description explains the purpose well. However, it lacks any explanation of the 'days' parameter's semantics or the expected return format. Given the complexity is low, this is a moderate gap.
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 has one parameter 'days' with a default but no description, and schema_description_coverage is 0%. The description never mentions the 'days' parameter or how it affects the results, providing no guidance on what values are appropriate or what the default means. For a single-parameter tool, this is a significant gap.
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 overnight/weekend gap statistics (average, min/max, latest bps) for all tickers, with the specific context that it measures drift while NASDAQ is closed. It does not explicitly name sibling tools for differentiation, so it loses a point for not directly distinguishing from hood_gaps_live or hood_gap_sessions.
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 this is for summary-level gap statistics across all tickers, and gives context about 24/5 trading. However, it offers no explicit guidance on when to use this tool versus alternative gap tools such as hood_gaps_live or hood_gap_sessions, and no 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.
