Dated catalysts that can move token prices while NASDAQ is closed (nights/weekends) — flattened from all research cards, sorted by date. For agents watching overnight gap and weekend risk.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds behavioral context beyond annotations: the data is 'flattened from all research cards' and 'sorted by date,' indicating aggregation and ordering. It also notes the catalysts 'can move token prices,' which gives insight into the data's significance.
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 only two sentences, front-loads the core purpose in the first sentence, and adds the use case in the second. Every phrase earns its place: 'Dated catalysts that can move token prices while NASDAQ is closed' is immediately informative, and the audience guidance is concise.
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 that this is a zero-parameter, read-only list tool with no output schema, the description is remarkably complete. It specifies the content (dated catalysts), the timing (off-hours), the aggregation source (all research cards), the sort order (by date), and the intended use case. This is sufficient for a tool of this simplicity.
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 tool has zero parameters, so parameter semantics are moot. The description does not mislead about inputs, and the empty schema is consistent. Baseline 4 applies for a 0-parameter tool.
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 identifies the tool as a list of dated catalysts that affect token prices during off-hours (NASDAQ closed). It distinguishes itself from sibling tools by specifying the niche focus on overnight and weekend risk, and the phrase 'flattened from all research cards' implies an aggregated view.
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 explicitly states when to use it ('For agents watching overnight gap and weekend risk') and the temporal context ('while NASDAQ is closed'). It does not explicitly name alternatives or exclusions, so it stops short of a 5, but the intended use case is clear.
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.
